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Background: In low- and middle-income countries (LMICs), such as Tanzania, the competency of healthcare providers critically influences the quality of pediatric care. To address this issue, we introduced Pediatric Acute Care Education (PACE), an adaptive learning program to enhance provider competency in Tanzania's guidelines for managing seriously ill children. Adaptive learning is a promising alternative to current in-service education, yet optimal implementation strategies in LMIC settings are unknown. Objectives: (1) To evaluate the initial PACE implementation in Mwanza, Tanzania, using the construct of normalization process theory (NPT); (2) To provide insights into its feasibility, acceptability, and scalability potential. Methods: Mixed-methods study involving healthcare providers at three facilities. Quantitative data was collected using the Normalization MeAsure Development (NoMAD) questionnaire, while qualitative data was gathered through in-depth interviews (IDIs) and focus groups discussions (FGDs). Results: Eighty-two healthcare providers completed the NoMAD survey. Additionally, 24 senior providers participated in IDIs, and 79 junior providers participated in FGDs. Coherence and cognitive participation were high, demonstrating that PACE is well understood and resonates with existing healthcare goals. Providers expressed a willingness to integrate PACE into their practices, distinguishing it from existing educational methods. However, challenges related to resources and infrastructure, particularly those affecting collective action, were noted. Early indicators point toward the potential for long-term sustainability of the PACE, but assessment of reflexive monitoring was limited due to the study's focus on PACE's initial implementation. Conclusion: This study offers vital insights into the feasibility and acceptability of implementing PACE in a Tanzanian context. While PACE aligns well with healthcare objectives, addressing resource and infrastructure challenges as well as conducting a longer-term study to assess reflexive monitoring is crucial for its successful implementation. Furthermore, the study underscores the value of the NPT as a framework for guiding implementation processes, with broader implications for implementation science and pediatric acute care in LMICs. [ABSTRACT FROM AUTHOR]

The native forestry industry is being reconsidered, or actively phased out, across most parts of Australia. This kind of transition has happened elsewhere in the world but, in Australia, there is no clarity from governments about what those forestry areas will become, from an ecological or legal perspective. In this research, we investigate the ecological, climatic, and governance contexts for this transition away from native forestry, placing our results in the context of the Global Biodiversity Framework's push to restore 30% of the Earth's land, coasts and oceans. We demonstrate important gaps in government planning for the future of former native forestry coupes, arguing that "after forestry" many of these places will need active intervention to recover these ecosystems and address historic land degradation. This case study combines legal analysis with a review of the restoration literature to illustrate the importance of the governance framework within which restoration science is evolving. We demonstrate the complexity of governing restoration at large scales as the climate changes, as well as potential opportunities to reconcile fragmented governance arrangements, to ensure that this transition results in resilient forest ecosystems in currently degraded areas. [ABSTRACT FROM AUTHOR]

Temporal adaptive convolution has demonstrated superior performance over static convolution techniques in video understanding. However, it needs to be improved in long-time series modeling and multi-scale feature-map adaptation. To address these challenges, we introduce spatio-temporal hybrid adaptive convolution (STHAC), designed to enhance the spatio-temporal modeling capabilities of convolution. This is achieved by learning a set of spatio-temporal calibration filters to mitigate the spatial invariance intrinsic to static convolution methods. Specifically, STHAC learns a linear combination of N adaptive filters by parallelizing two lightweight attention branches. The resulting linearly mixed filters incorporate spatial multi-scale prior knowledge and long-range temporal dependencies. These spatio-temporal calibration filters modulate each frame's static convolutional weight parameters, thereby endowing static convolution with spatial multi-scale adaptability and long-range temporal modeling capabilities. Compared to other dynamic convolution methods, our proposed calibration filters require fewer parameters and incur lower computational complexity. Moreover, we introduce an Omni-dimensional aggregation module to augment the spatio-temporal modeling capacity of STHAC. When combined with STHAC, this aggregation module forms the spatio-temporal adaptive module (STAM) that can replace static convolution. We implement a spatio-temporal dynamic network based on STAM to validate our approach. Experimental results indicate that our model is competitive with state-of-the-art convolutional neural network architectures on action recognition benchmarks such as Kinetics-400(K400) and Something-Something V2(SSV2). [ABSTRACT FROM AUTHOR]

[Display omitted] In recent times, digital technology has played a pivotal role in advancing postgraduate orthodontic education. This article comprehensively overviews digital technology's various applications, benefits, and drawbacks in postgraduate orthodontic education. Further, it examines its transformative impact on students. The primary focus of this review is on reshaping traditional teaching methods and learning experiences through innovative tools and techniques. Beginning with online learning platforms, virtual classrooms, and webinars, a comprehensive review of their role in facilitating remote education and creating interactive learning environments is provided. It also explores the integration of interactive content and multimedia in post-graduate education and emphasizes their ability to enhance students' engagement and comprehension. Augmented and virtual reality (AR/VR) technologies will also be explored, with emphasis on their role in revolutionizing practical learning experiences within postgraduate curricula. Additionally, the article discusses the part that adaptive learning and artificial intelligence (AI) play in utilizing data analytics to personalize education and optimize learning outcomes. The influence of social media platforms, mobile applications, digital libraries, and open educational resources (OER) on collaborative learning environments will be reviewed, showcasing their impact on fostering collaboration and accessibility among postgraduate students. Collaborative tools, simulations, and e-labs' role in enhancing hands-on learning experiences are highlighted. Finally, a critical analysis of the benefits and drawbacks of integrating digital technology in orthodontic education, addressing ethical and legal concerns arising from its implementation, will also be overviewed. In summary, this article explores a nuanced understanding of the transformative potential of digital technology in postgraduate orthodontic education. It encompasses various applications, benefits, challenges, and ethical considerations, guiding stakeholders toward informed integration strategies for these technologies in higher education. [ABSTRACT FROM AUTHOR]

Adaptive learning aims to tailor the learning experience, including content, navigation, presentation, and strategies, based on learners' cognitive and affective factors. However, many existing adaptive learning systems often fail to meet the diverse needs and preferences of learners, typically relying on unique factors such as learning style. To address this, a robust framework is proposed, incorporating an artificial intelligence (AI) driven adaptive learning model capable of considering multiple factors, including past performance, hobbies, and learning style. The approach leverages the k-means clustering algorithm to group learners with similar leisure interests and incorporates Support Vector Regression to predict student performance, utilizing demographic data and past performance as metrics. Furthermore, the implementation of the Personalized Study Guide plugin, based on k-means clustering, enables the system to create personalized learning paths tailored to individual learning styles. By adopting this approach, the objective is to enhance learner engagement and performance through the development of a robust adaptive learning system. [ABSTRACT FROM AUTHOR]

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In response to the current situation of poor adaptive learning performance in Agent production and sales negotiation and dynamic changes in negotiation environment, the author proposes a method based on machine learning algorithms. Consider the impact of conflict level, cooperation possibility, and negotiation remaining time on negotiations in a dynamic negotiation environment, and use the entropy method to determine the weights of three influencing factors and perform linear weighting. Based on the differences in current negotiation topics, a concession amplitude prediction model based on dynamic selective ensemble learning is constructed, and an optimization strategy for supply chain production and sales negotiation is proposed. The experimental results indicate that, in the adaptive negotiation strategy of a regular SVM single learning machine, the joint utility of the most successfully negotiated agents falls within the interval [0.55, 0.70], while the author's ensemble learning strategy mainly focuses on [0.6, 0.8], the author's strategy is relatively superior to ordinary learning strategies in terms of both the number of successfully negotiated agents and the joint utility. Compared with the single learning machine negotiation strategy, this strategy improves the success rate and joint utility of Agent adaptive learning, and ensures the benefits of both production and sales in the supply chain, achieving a mutually beneficial situation for both parties in cooperation. [ABSTRACT FROM AUTHOR]

Personalized learning, a pedagogical approach tailored to individual needs and capacities, has garnered considerable attention in the era of artificial intelligence (AI) and the fourth industrial revolution. This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers. Following PRISMA guidelines, a thorough search was conducted across major peer-reviewed journal databases, resulting in the inclusion of 102 relevant studies published between 2013 and 2022. A combination of qualitative and quantitative analyses, employing categorization and frequency analysis techniques, was performed to discern patterns and insights from the literature. The findings of this review highlight several critical drivers that contribute to the effectiveness of personalized learning, both from a broad view of education and in the specific context of e-learning. Firstly, recognizing and accounting for individual student characteristics is foundational to tailoring educational experiences. Secondly, personalizing content delivery and instructional methods ensures that learning materials resonate with learners' preferences and aptitudes. Thirdly, customizing assessment and feedback mechanisms enables educators to provide timely and relevant guidance to learners. Additionally, tailoring user interfaces and learning environments fosters engagement and accessibility, catering to diverse learning styles and needs. Moreover, the integration of AI presents significant opportunities to enhance personalized learning. AI-driven solutions offer capabilities such as automated learner profiling, adaptive content recommendation, realtime assessment, and the development of intelligent user interfaces, thereby augmenting the personalization of learning experiences. However, the successful adoption of AI in personalized learning requires addressing various challenges, including the need to develop educators' competencies, refine theoretical frameworks, and navigate ethical considerations surrounding data privacy and bias. By providing a comprehensive understanding of the drivers and implications of AI-driven personalized learning, this review offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era. Leveraging the transformative potential of AI while upholding robust pedagogical principles, personalized learning holds the promise of unlocking tailored educational experiences that maximize individual potential and relevance in the digital economy. [ABSTRACT FROM AUTHOR]

Categorical learning is important and often challenging in both specialized domains, such as medical image interpretation, and commonplace ones, such as face recognition. Research has shown that comparing items from different categories can enhance the learning of perceptual classifications, particularly when those categories appear highly similar. Here, we developed and tested novel adaptively triggered comparisons (ATCs), in which errors produced during interactive learning dynamically prompted the presentation of active comparison trials. In a facial identity paradigm, undergraduate participants learned to recognize and name varying views of 22 unknown people. In Experiment 1, single-item classification trials were compared to a condition in which ATC trials were generated whenever a participant repeatedly confused two faces. Comparison trials required discrimination between simultaneously presented exemplars from the confused categories. In Experiment 2, an ATC condition was compared to a non-adaptive comparison condition. Participants learned to accuracy and speed criteria, and completed immediate and delayed posttests. ATCs substantially enhanced learning efficiency in both experiments. These studies, using a novel adaptive procedure guided by each learner’s performance, show that adaptively triggered comparisons improve category learning. [ABSTRACT FROM AUTHOR]

This paper presents an interdisciplinary framework, Machine Psychology, which integrates principles from operant learning psychology with a particular Artificial Intelligence model, the Non-Axiomatic Reasoning System (NARS), to advance Artificial General Intelligence (AGI) research. Central to this framework is the assumption that adaptation is fundamental to both biological and artificial intelligence, and can be understood using operant conditioning principles. The study evaluates this approach through three operant learning tasks using OpenNARS for Applications (ONA): simple discrimination, changing contingencies, and conditional discrimination tasks. In the simple discrimination task, NARS demonstrated rapid learning, achieving 100% correct responses during training and testing phases. The changing contingencies task illustrated NARS's adaptability, as it successfully adjusted its behavior when task conditions were reversed. In the conditional discrimination task, NARS managed complex learning scenarios, achieving high accuracy by forming and utilizing complex hypotheses based on conditional cues. These results validate the use of operant conditioning as a framework for developing adaptive AGI systems. NARS's ability to function under conditions of insufficient knowledge and resources, combined with its sensorimotor reasoning capabilities, positions it as a robust model for AGI. The Machine Psychology framework, by implementing aspects of natural intelligence such as continuous learning and goal-driven behavior, provides a scalable and flexible approach for real-world applications. Future research should explore using enhanced NARS systems, more advanced tasks and applying this framework to diverse, complex tasks to further advance the development of human-level AI. [ABSTRACT FROM AUTHOR]

Objective: In this study, we examined whether vestibular migraine, as a source of increased perceptual uncertainty due to the associated dizziness, interferes with adaptive learning. Methods: The IOWA gambling task (IGT) was used to assess adaptive learning in both healthy controls and patients with migraine-related dizziness. Participants were presented with four decks of cards (A, B, C, and D) and requested to select a card over 100 trials. Participants received a monetary reward or a penalty with equal probability when they selected a card. Card decks A and B (high-risk decks) involved high rewards (win £100) and high penalties (lose £250), whereas C and D (low-risk decks; favorable reward-to-punishment ratio) involved lower rewards (win £50) and penalties (lose £50). Task success required participants to decide (i.e., adaptively learn) through the feedback they received that C and D were the advantageous decks. Results: The study revealed that patients with vestibular migraine selected more high-risk cards than the control group. Chronic vestibular migraine patients showed delayed improvement in task performance than those with acute presentation. Only in acute vestibular migraine patients, we observed that impaired learning positively correlated with measures of dizzy symptoms. Conclusion: The findings of this study have clinical implications for how vestibular migraine can affect behavioural adaption in patients, either directly through altered perception or indirectly by impacting cognitive processes that can result in maladaptive behavior. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss the application of contemporary AI tools and technologies in education. The paper will mainly focus on a close examination of relevant academic literature as well as the current situation and methods of AI implementation in different sectors, finally, in education. Particularly, it was crucial that there were identified the major directions of applying AI in learning with an emphasis on learners and educators being the basic aspects of AI use within modern educational approaches. Secondly, some benefits and drawbacks concerning AI implementations in education supported by experiences from industry leaders and specific satisfaction measures among existing users, are analyzed. The most significant part covers integrating findings that show facts about artificial intelligence being used in education for 2024 like top online learning statistics for 2024, top AI statistics for 2024, and best AI plagiarism checkers for teachers in 2024 respectively. This includes evaluating teacher or instructor preparation skills to employ artificial intelligence (AI) tools and technology as well as ways students can learn how to use them without misuse towards quality education improvement. [ABSTRACT FROM AUTHOR]

Using anthropological theory, this paper examines human–android interactions (HAI) as an emerging aspect of android science. These interactions are described in terms of adaptive learning (which is largely subconscious). This article is based on the observations reported and supplementary data from two studies that took place in Japan with a teleoperated android robot called Telenoid in the socialization of school children and older adults. We argue that interacting with androids brings about a special context, an interval, and a space/time for reflection and imagination that was not there before. During the interaction something happens. There is adaptive learning and as a result, both children and older adults accepted Telenoid, and the children and older adults accepted each other. Using frames of play and ritual, we make sense and 'capture' moments of adaptive learning, and the feedback that elicits a social response from all study participants that results in self-efficacy and socialization. While "ritual" refers to the application of what has been learned and "play" means that there are no obvious consequences of what has been learned. This analysis illuminates new understanding about the uncanny valley, cultural robotics and the therapeutic potential of HAI. This has implications for the acceptance of androids in 'socialized roles' and gives us insight into the subconscious adaptive learning processes that must take place within humans to accept androids into our society. This approach aims to provides a clearer conceptual basis and vocabulary for further research of android and humanoid development. [ABSTRACT FROM AUTHOR]

As students' affective states can differ between learning that occurs in traditional classrooms when compared with learning that takes place in extended reality and immersive learning environments, it is important to examine the role of affective computing. Therefore, this study aims to provide an overview regarding the use of affective computing in the context of augmented reality, virtual reality, and immersive learning environments and the metaverse. Hence, it examines and presents the state of the art through a review and bibliometric analysis of 188 documents of the existing literature from Scopus and Web of Science (WoS) from 2005 to 2023. The study follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement to identify and select relevant to the topic documents. In addition to the analysis of the existing literature, emerging topics and themes are identified and future research directions are presented. The significant role of affective computing within augmented reality and virtual reality environments arose. Their ability to offer engaging and interactive learning experiences while also being able to recognize, monitor, and respond to students' affective states and to consider their emotions, personalities, characteristics, knowledge, and preferences to provide adaptive and personalized learning was evident. Additionally, their potential to enrich educational activities, increase learning outcomes, and support special education emerged. When augmented reality, virtual reality, and immersive learning environments capitalize on affective computing, meaningful learning can occur and self-regulated learning can be promoted. [ABSTRACT FROM AUTHOR]

Adaptive learning is a teaching approach aiming to personalize the learning experience for each student. In Computer Science Education (CSE), Adaptive Learning Systems (ALS) can provide students with customized lessons, exercises, and assessments based on their previous knowledge, strengths, and weaknesses. Some literature reviews focus on adaptive learning techniques, but none specifically analyze the methods used in adaptive systems in CSE. Our overall objective is to identify the computing techniques that are implemented within ALS to provide adaptive computer education in higher education. We conducted a scoping review (SR) based on the Guidelines for performing Systematic Literature Reviews in Software Engineering, defining six research questions and a search string. We performed searches in four databases, retrieving 512 documents. After applying the selection criteria, we selected 29 works. The results present quantitative and qualitative data from those 29 studies. Most research has used adaptive learning in programming education (12 articles) and databases (9 papers). Nine articles used learning styles as the core of adaptation, with the Felder-Silverman model being used by seven of them. The most commonly used approach was the rule-based system approach built by experts within the proposal, which was used in 17.24% of the articles. Clustering, collaborative filtering, fuzzy logic, and K-NN were used in 10.34% of the proposals. The positive aspect of "delivering adaptive content" was pointed out in seven articles. As for negative aspects, the problem of lack of student engagement was mentioned in four articles. This study discusses the application of dynamic and static models based on learning styles (LS) and the challenges involved in their use. Additionally, another aspect pointed out is that it is crucial to carefully consider pedagogical aspects in learning systems and have mixed development teams. The study also discusses student motivation and the use of gamification elements as a strategy to engage students in electronic learning systems. Finally, the results also indicate that the level of student knowledge is typically represented by a student model (SM), and the growing trend is the use of hybrid SMs. The research identified the most studied areas in ALS in CSE, such as computer programming and databases. The most common techniques used in ALS were rule-based systems built by experts, fuzzy logic, and clustering. The most common feature of ALS identified was learning resources, and the majority of studies focused on investigating the impact of system accuracy. This research can help researchers design better adaptive learning systems and understand challenges that need to be addressed. [ABSTRACT FROM AUTHOR]

Deep learning-based approaches have gained popularity for many applications in recent years and have become the state-of-the-art method in machine learning applications. However, most deep learning applications manipulate static data sets that do not vary over time. This is not the case for several significant recent applications, particularly for online learning on data streams that require real-time adjustment to the latest data context. This paper presents an architecture derived from the theory of transfer learning utilizing Extreme Learning Machine (ELM) and Recurrent Neural Network (RNN) methods. Unlike most data stream applications, when receiving high volumes of data at a high velocity, the proposed method does not store and reuse previous observations. The proposed model uses a Deep Echo State Network (DeepESN) for the primary classification task to compensate for the lack of past data. More precisely, instead of the data itself, we propagate generative models so that historical training data that is not stored can be regenerated. Before the DeepESN block, a layer of pre-trained ELMs was used to have generic feature engineering. The proposed method was tested on well-known benchmark data sets, and it outperformed the state-of-the-art models by an accuracy improvement ranging from 0.02 to 3.3 percent. The impact of data set regeneration on the learning process was studied using different RNN approaches. [ABSTRACT FROM AUTHOR]

Digital Information and Communication Technologies act as partners in the educational context, making it more dynamic through Virtual Learning Environments (VLEs). The adaptive system is based on technological solutions/tools, which allow the customization of teaching processes according to the student's singularities. Therefore, we conducted a systematic literature review (SLR) to elucidate which educational performance indicators best guide adaptive learning in virtual learning environments. To this end, we adopted the principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) as a systematic review protocol, and as operational support, we used the online tool Parsifal. The initial database search - IEEE, ACM, and Scopus - returned 276 articles. After filtering based on the protocol, 16 articles remained part of the analysis and discussion corpus. The RSL results indicate that most of the indicators used to guide activities are based on the correctness and error of the questions. This shows that there is still much to be implemented in learning adaptability in virtual environments; for a more holistic assessment of learning, it is necessary to consider an integrated set of these indicators and not just individualized analyses. [ABSTRACT FROM AUTHOR]

Adaptive learning technologies are closely related to learners' self‐regulatory processes in individual and collaborative learning. This study presents the outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self‐regulation and shared regulation of learning in collaborative settings. We provide an overview of what and how adaptive technologies have been used to understand and promote self‐regulated learning in collaborative contexts. A search resulted in 38 papers being analysed. Specifically, we identified the seven main objectives (feedback and scaffolding, self‐regulatory skills and strategies, learning trajectories, collaborative learning processes, adaptation and regulation, self‐assessment, and help‐seeking behaviour) that the adaptive technology research has been focusing on. We also summarize the implications derived from the reviewed papers and frame them within seven thematic areas. Finally, this review stresses that future research should consider developing a converging theoretical framework that would enable concrete monitoring and support for self‐regulation and socially shared regulation of learning. Our findings set a baseline to support the adoption and proliferation of adaptive learning technology within self‐regulated learning research and development. Practitioner notesWhat is already known about this topic By providing personalized and learner‐centric adaptive learning environments (ADLEs), adaptive learning technology can support and foster self‐regulated learning (SRL) practices.It is possible to create a more student‐centred and effective learning environment by combining adaptive learning and collaborative learning.Socially shared regulatory activities can involve planning, monitoring, controlling and reflecting on a group's learning processes.What this paper adds Provides a systematic literature review of empirical evidence on ADLEs, SRL and socially shared regulation of learning (SSRL) in collaborative contexts.Summarizes the insights on (S)SRL through ADLEs in collaborative learning.Identifies challenges and opportunities for ADLEs to support (S)SRL in collaborative learning.Implications for practice and/or policy Learning analytics and educational technology researchers will be able to use the systematic review as a guide for future research.Learning analytics and educational technology practitioners will be able to use the systematic review as a summary of the field's current state. [ABSTRACT FROM AUTHOR]

A compact MOSFET-C floating/grounded meminductor emulator (MIE) model is presented for high operating frequency and low power operation. The proposed MIE uses only 22 MOSFETs and two capacitors. Its performance is theoretically analyzed and rigorously verified using the Cadence Virtuoso software and hardware prototypes. The proposed MIE operates appropriately for a wide range of frequencies up to 5 MHz with $590 \mu \text{W}$ power consumption at a 180nm CMOS technology node and manifests important signature properties. The MIE layout area in 180 nm CMOS technology is $13107.5 \mu \text{m}^{2}$. To analyze the effects of statistical variations in MIE elements, extensive Monte Carlo simulations have been performed to demonstrate the robustness of the proposed MIE. For experimental validation, hardware prototypes have been developed and tested successfully. An MIE-based adaptive learning neuromorphic circuit is presented to show that it can mimic the behavioral responses of amoeba under varying environments such as temperature. [ABSTRACT FROM AUTHOR]

In the advent of e-learning, understanding student engagement and reaction is crucial for improving the quality of education and enhancing the learning rate. With the advancement of computer vision technologies, there is a significant opportunity to analyze and interpret student reactions in a non-intrusive manner. This study proposes a novel framework employing Faster R-CNN integrated with DenseNet architecture for real-time detection of student facial reactions during e-learning sessions. The proposed method leverages the strengths of Faster R-CNN in generating high-quality region proposals for object detection tasks, coupled with the DenseNet's efficiency in feature propagation and reduction in the number of parameters, which is well-suited for processing the intricate patterns in facial expressions. Our approach begins with the application of Faster R-CNN to extract potential facial regions with high accuracy and reduced computational cost. The integration of DenseNet as a backbone for feature extraction within Faster R-CNN capitalizes on its densely connected convolutional networks, ensuring maximum information flow between layers in the network. By doing so, the system becomes exceptionally adept at recognizing subtle changes in facial features that indicate various student reactions, such as confusion, engagement, or boredom. We conducted a series of experiments using a diverse dataset of e-learning interactions, collected under various lighting conditions and involving multiple ethnicities to ensure robustness and generalizability. The model was trained and validated on this dataset, and the results demonstrate a significant improvement in detection rates of student reactions compared to existing methods. [ABSTRACT FROM AUTHOR]

The age of pervasive computing has initiated a boom in the development of adaptive context-aware learning environments (ACALEs), i.e., systems that are capable of detecting a learner's context and providing adaptive learning services based on this context. Many of the existing educational systems were developed as standalone applications for specific or a small range of adaptive educational scenarios. It would be extremely helpful for developers and educators to have a unified framework that provides an infrastructure for the development of ACALEs. In this study, we propose Lightlore—an adaptation framework that enables the development of different types of ACELEs for a wide range of learning scenarios in formal and informal settings. We first used scenario-based design (SBD) as the design methodology for creating a conceptual model of Lightlore. Educational scenarios were adopted from the results of a previous literature review. We then developed a proof-of-concept implementation of Lightlore, with a hypermedia system for learning data structures that uses the adaptation service of Lightlore. This implementation is essentially an adaptation infrastructure and a programming API for creating new (or transforming existing) adaptive and context-aware educational services. It exploits the experience API (xAPI), a modern e-learning standard and learning record store, thus making coupling with existing learning environments easier. We expect that diverse types of users will benefit from using Lightlore, such as learners, educators, learning environment developers, and researchers on educational technologies. [ABSTRACT FROM AUTHOR]

Abstract Background In low- and middle-income countries (LMICs), such as Tanzania, the competency of healthcare providers critically influences the quality of pediatric care. To address this issue, we introduced Pediatric Acute Care Education (PACE), an adaptive learning program to enhance provider competency in Tanzania’s guidelines for managing seriously ill children. Adaptive learning is a promising alternative to current in-service education, yet optimal implementation strategies in LMIC settings are unknown. Objectives (1) To evaluate the initial PACE implementation in Mwanza, Tanzania, using the construct of normalization process theory (NPT); (2) To provide insights into its feasibility, acceptability, and scalability potential. Methods Mixed-methods study involving healthcare providers at three facilities. Quantitative data was collected using the Normalization MeAsure Development (NoMAD) questionnaire, while qualitative data was gathered through in-depth interviews (IDIs) and focus groups discussions (FGDs). Results Eighty-two healthcare providers completed the NoMAD survey. Additionally, 24 senior providers participated in IDIs, and 79 junior providers participated in FGDs. Coherence and cognitive participation were high, demonstrating that PACE is well understood and resonates with existing healthcare goals. Providers expressed a willingness to integrate PACE into their practices, distinguishing it from existing educational methods. However, challenges related to resources and infrastructure, particularly those affecting collective action, were noted. Early indicators point toward the potential for long-term sustainability of the PACE, but assessment of reflexive monitoring was limited due to the study’s focus on PACE’s initial implementation. Conclusion This study offers vital insights into the feasibility and acceptability of implementing PACE in a Tanzanian context. While PACE aligns well with healthcare objectives, addressing resource and infrastructure challenges as well as conducting a longer-term study to assess reflexive monitoring is crucial for its successful implementation. Furthermore, the study underscores the value of the NPT as a framework for guiding implementation processes, with broader implications for implementation science and pediatric acute care in LMICs.

Abstract Categorical learning is important and often challenging in both specialized domains, such as medical image interpretation, and commonplace ones, such as face recognition. Research has shown that comparing items from different categories can enhance the learning of perceptual classifications, particularly when those categories appear highly similar. Here, we developed and tested novel adaptively triggered comparisons (ATCs), in which errors produced during interactive learning dynamically prompted the presentation of active comparison trials. In a facial identity paradigm, undergraduate participants learned to recognize and name varying views of 22 unknown people. In Experiment 1, single-item classification trials were compared to a condition in which ATC trials were generated whenever a participant repeatedly confused two faces. Comparison trials required discrimination between simultaneously presented exemplars from the confused categories. In Experiment 2, an ATC condition was compared to a non-adaptive comparison condition. Participants learned to accuracy and speed criteria, and completed immediate and delayed posttests. ATCs substantially enhanced learning efficiency in both experiments. These studies, using a novel adaptive procedure guided by each learner’s performance, show that adaptively triggered comparisons improve category learning.

Children born after 2010 are labelled as members of Generation Alpha, who currently pursue their primary education. Gamification and game-based learning methodologies have gained popularity in the global education sector in the recent past. The goal of this study is to investigate the present state of adopting gamification and game-based learning for primary education student cohorts, with the aid of recent peer-reviewed research publications. By employing a systematic mapping design, reviewed paper results are categorized and further evaluated in relation to the attributes such as type of gamification, game mechanics and elements used, evaluation context, type of experimental outcome, gamified academic subject area, and type of application. With the aid of review results, this study proposes a mapping of gamification learning mechanics, suited to address Generation Alpha traits. It also recommends future research directions, which include investigating the realistic learning preferences of Generation Alpha, the need for designing novel educational game elements and adaptive gamification learning strategies, and the importance of long-term studies with diverse learner samples. The findings of this study will support prospective stakeholders, such as researchers, educators, parents, and educational policymakers, to utilize gamification approaches effectively in educating Generation Alpha.

Different recognitions become more mature and have been widely applied for EFL learning. Each recognition also has its specific features useful for EFL descriptive writing. The pictorial and verbal representations and current context found in image-to-text recognition (ITR), translated speech-to-text recognition (TSTR), and location-to-text recognition (LTR) respectively could be beneficial for EFL descriptive writing concerning enriching lexical language resources, developing writing ideas, and the others. This study investigated the influences of multiple recognitions and their affordances for EFL descriptive writing in authentic context learning via their generations and usages. An experiment had been conducted for twelve weeks in a vocational high school in Taiwan. Three tests, 1291 essays, and open-ended questionnaires were collected and analyzed. The results revealed that multiple recognitions in the context improve the reasoning, organization, communication, and convention aspects of EFL descriptive writing. Although the ITR affordance is less than TSTR, it significantly influences the appropriate vocabulary usage, the detailed content developments, and the various sentence elaboration of the writing. The TSTR affordance is better than the other recognitions because of its convenience for making more sentences; however, it does not immediately influence EFL descriptive writing. The LTR affordance is similar to ITR, and it can be useful for the usage of other recognitions concerning the direction of the general ideas, main subjects, and related objects addressed in the writing. Although the recognition accuracy needs to be improved, the integration of multiple recognitions has big potential and should be widely applied for EFL writing considering the affordances and significant influences.

Abstract Background Personalised learning, an educational approach that tailors teaching and learning to individual needs and preferences, has gained attention in recent years, particularly in higher education. Advances in educational technology have facilitated the implementation of personalised learning in various contexts. Despite its potential benefits, the literature on personalised learning in health sciences higher education remains scattered and heterogeneous. This scoping review aims to identify and map the current literature on personalised learning in health sciences higher education and its definition, implementation strategies, benefits, and limitations. Methods A comprehensive search of electronic databases, PubMed, Scopus, Google Scholar, Educational Research Complete, and Journal Storage (JSTOR), will be conducted to identify relevant articles. The search will be limited to articles published in the English language between 2000 and 2023. The search strategy will be designed and adapted for each database using a combination of keywords and subject headings related to personalised learning and health sciences higher education. Eligibility criteria will be applied to screen and select articles. Data extraction and quality assessment will be performed, and thematic synthesis will be used to analyse the extracted data. Discussion The results of the scoping review will present a comprehensive and coherent overview of the literature on personalised learning in health sciences higher education. Key themes and topics related to personalised learning, its definitions, models, implementation strategies, benefits, and limitations, will be identified. The geographical and temporal distribution of research on personalised learning in health sciences higher education will also be described. This scoping review will provide a structured synthesis of the available evidence on personalised learning in health sciences higher education, highlighting potential gaps and areas for future research. The findings will contribute to ongoing scholarly and policy debates on personalised learning in higher education, informing the development of best practices, guidelines, and future research agendas.

Computer aided diagnosis methods play an important role in Attention Deficit Hyperactivity Disorder (ADHD) identification. Dynamic functional connectivity (dFC) analysis has been widely used for ADHD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), which can help capture abnormalities of brain activity. However, most existing dFC-based methods only focus on dependencies between two adjacent timestamps, ignoring global dynamic evolution patterns. Furthermore, the majority of these methods fail to adaptively learn dFCs. In this paper, we propose an adaptive spatial-temporal neural network (ASTNet) comprising three modules for ADHD identification based on rs-fMRI time series. Specifically, we first partition rs-fMRI time series into multiple segments using non-overlapping sliding windows. Then, adaptive functional connectivity generation (AFCG) is used to model spatial relationships among regions-of-interest (ROIs) with adaptive dFCs as input. Finally, we employ a temporal dependency mining (TDM) module which combines local and global branches to capture global temporal dependencies from the spatially-dependent pattern sequences. Experimental results on the ADHD-200 dataset demonstrate the superiority of the proposed ASTNet over competing approaches in automated ADHD classification. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Anatomy education plays a critical role in medical practice, and the level of anatomical knowledge among students and physicians significantly impacts patient care. This article presents a pilot project aimed at exploring the effectiveness of the Area9's Rhapsode platform, an intelligent tutoring system that uses artificial intelligence (AI) to personalize learning and collect data on mastery acquisition. METHODS: The study focused on liver anatomy (microscopic and macroscopic anatomy, embryology, clinical anatomy) and employed a flipped classroom approach, incorporating adaptive learning modules and an interactive in-class session. A total of 123 first-year medicine students (55 M/68F) participated to the study. Content and resources of the module were adaptable to various digital devices. Statistics were compiled based, on the one hand, on the measurement of mastery for every single learning objective provided automatically by the platform via the student interactions with the system probes (questions); on the other hand, metacognition data were worked out by crossing mastery data with the self-awareness declared in every question and learning resource by each learner. RESULTS AND CONCLUSIONS: At the outset of the study, students displayed a 18.11% level of conscious incompetence and a 19.43% level of unconscious incompetence. Additionally, 50.86% of students demonstrated conscious competence. By the conclusion of the learning module, the level of conscious incompetence had decreased to 1.87%, and 98.73% of students exhibited conscious mastery of the materials. The results demonstrated improved learning quality, positive repurposing of study time, enhanced metacognitive awareness among students, with most students demonstrating conscious mastery of the materials and a clear understanding of their level of competence. This approach, by providing valuable insights into the potential of AI-based adaptive learning systems in anatomy education, could address the challenges posed by limited teaching hours, shortage of anatomist, and the need for individualized instruction. [ABSTRACT FROM AUTHOR]

Drug‐drug interaction (DDI) event prediction is a challenging problem, and accurate prediction of DDI events is critical to patient health and new drug development. Recently, many machine learning‐based techniques have been proposed for predicting DDI events. However, most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature information. To address these limitations, we propose a DDI‐Transform neural network framework for DDI event prediction. In DDI‐Transform, we design a drug structure information feature extraction module and a drug bind‐protein feature extraction module to obtain multidimensional feature information. A stack of DDI‐Transform layers in the DDI‐Transform network module are then used for adaptive learning, thus adaptively selecting the effective feature information for prediction. The results show that DDI‐Transform can accurately predict DDI events and outperform the state‐of‐the‐art models. Results on different scale datasets confirm the robustness of the method. [ABSTRACT FROM AUTHOR]

This paper presents one of the first marketing applications of molecular genetics. We report evidence that salespeople's genetic variants linked to educational attainment predict sales performance. Both genetics and selling effort contribute to sales performance, whereas genetics contribute more than personality traits. We further show that adaptive learning, as captured in salespeople's customer orientation and opportunity recognition skills, may explain the gene-sales relationship. We discuss the implications of these findings for sales management and the value of genetic research for the marketing field. This paper was accepted by Matthew Shum, marketing. Funding: S. Gong acknowledges financial support from the NSFC [Grant 71972040] and Fundamental Research Funds for the Central Universities [2022NTSS43]. Q. Li acknowledges financial support from the NSFC [Grant 72072014] and Young Talent Program of Beijing Foreign Studies University. S. Su acknowledges financial support from the NSFC [Grant 71872016]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4879. [ABSTRACT FROM AUTHOR]

Adaptive learning technology is an educational method that uses artificial intelligence and computer algorithms. This learning system can manage students' interaction pattern during learning activities. The use of adaptive learning technology is able to change students from just receiving information to an active and collaborative part in the learning process. This research was conducted with the aim of improving the quality of education in Indonesia by encouraging teachers to use this technology. This research also aims to provide a better understanding of the potential and weaknesses of adaptive learning technology in improving students' concept understanding as well as providing stronger guidance for curriculum development and better educational practices. The method used in this research is quantitative method. This method is a way of collecting numerical data that can be tested. Data is collected through the distribution of questionnaires addressed to students. Furthermore, the data that has been collected from the distribution of the questionnaire, will be accessible in Excel format which can then be processed with SPSS. From the results of the study, it can be seen that the impact of using adaptive learning technology shows that adaptive learning technology can improve the quality of education. Research shows that with the use of adaptive learning technology, it can change teaching methods, learning materials, and can find out the level of learning difficulties faced by these students. From this study, researchers can conclude that the impact of using adaptive learning technology, can improve student understanding and achievement and has the potential to improve the quality of education. with the existence of adaptive learning technology, it is able to increase student involvement and motivation in learning, so that student understanding in learning can be achieved well. [ABSTRACT FROM AUTHOR]

In sand-dust weather, the influence of sand-dust particles on imaging equipment often results in images with color deviation, blurring, and low contrast, among other issues. These problems making many traditional image restoration methods unable to accurately estimate the semantic information of the images and consequently resulting in poor restoration of clear images. Most current image restoration methods in the field of deep learning are based on supervised learning, which requires pairing and labeling a large amount of data, and the possibility of manual annotation errors. In light of this, we propose an unsupervised sand-dust image restoration network. The overall model adopts an improved CycleGAN to fit unpaired sand-dust images. Firstly, multiscale skip connections in the multiscale cascaded attention module are used to enhance the feature fusion effect after downsampling. Secondly, multi-head convolutional attention with multiple input concatenations is employed, with each head using different kernel sizes to improve the ability to restore detail information. Finally, the adaptive decoder-encoder module is used to achieve adaptive fitting of the model and output the restored image. According to the experiments conducted on the dataset, the qualitative and quantitative indicators of USIR-Net are superior to the selected comparison algorithms, furthermore, in additional experiments conducted on haze removal and underwater image enhancement, we have demonstrated the wide applicability of our model. [ABSTRACT FROM AUTHOR]

Gamification has gained an increasing attention from researchers and practitioners in various domains including education as it can increase learners' engagement and motivation. However, little is known about how educational gamification experiences can be influenced by learners' characteristics. Therefore, this study provides a systematic meta-review of empirical studies related to learners' characteristics and educational gamification experiences. The obtained results from the meta-analysis of forty related articles are: (a) learners' psychological and behavioral outcomes were affected by learners' characteristics in educational gamification systems; (b) quantitative methods, using questionnaires, are the most used method to measure the effect of learners' characteristics on their learning outcomes in educational gamification systems, and this needs to be changed to the new potential of using educational big data and learning analytics approaches; (c) personality traits is the most investigated characteristic followed by player types, but there is a need to further investigate other important factors of learners' characteristics, such as working memory capacity and age; and, (d) a set of game design guidelines that should be taken into consideration while designing educational gamification catering individual difference were proposed. [ABSTRACT FROM AUTHOR]

There are many problems faced by students in an online discussion forum, specifically regarding platforms that accommodate a large number of students such as the Massive Open Online Courses (MOOCs) platform. One of the problems is that the teacher cannot respond one by one to student comments that may contain questions or ideas. Therefore, the objectives of this research were to develop an adaptive instructional scaffolding feature that can be applied to online discussion forums and to perform usability measurements for this scaffolding. This feature provides an immediate response to student posts in the discussion forum in the form of article recommendations sourced from the Scopus database. These recommendations are based on the students’ comments posted on online discussion forums. The development model used in this study is the instructional model ADDIE (Analyze, Design, Development, Implementation, Evaluation). To test the usability of the developed features, usability measurement using the usefulness, satisfaction, ease of use (USE) questionnaire was employed. The survey was conducted with 137 students at a public university in Indonesia. The results of the measurement showed that the usability level of the features we developed was shown to be high. [ABSTRACT FROM AUTHOR]

AbstractA novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. [ABSTRACT FROM AUTHOR]

Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy. [ABSTRACT FROM AUTHOR]

Background: Personalised learning, an educational approach that tailors teaching and learning to individual needs and preferences, has gained attention in recent years, particularly in higher education. Advances in educational technology have facilitated the implementation of personalised learning in various contexts. Despite its potential benefits, the literature on personalised learning in health sciences higher education remains scattered and heterogeneous. This scoping review aims to identify and map the current literature on personalised learning in health sciences higher education and its definition, implementation strategies, benefits, and limitations. Methods: A comprehensive search of electronic databases, PubMed, Scopus, Google Scholar, Educational Research Complete, and Journal Storage (JSTOR), will be conducted to identify relevant articles. The search will be limited to articles published in the English language between 2000 and 2023. The search strategy will be designed and adapted for each database using a combination of keywords and subject headings related to personalised learning and health sciences higher education. Eligibility criteria will be applied to screen and select articles. Data extraction and quality assessment will be performed, and thematic synthesis will be used to analyse the extracted data. Discussion: The results of the scoping review will present a comprehensive and coherent overview of the literature on personalised learning in health sciences higher education. Key themes and topics related to personalised learning, its definitions, models, implementation strategies, benefits, and limitations, will be identified. The geographical and temporal distribution of research on personalised learning in health sciences higher education will also be described. This scoping review will provide a structured synthesis of the available evidence on personalised learning in health sciences higher education, highlighting potential gaps and areas for future research. The findings will contribute to ongoing scholarly and policy debates on personalised learning in higher education, informing the development of best practices, guidelines, and future research agendas. [ABSTRACT FROM AUTHOR]

Children born after 2010 are labelled as members of Generation Alpha, who currently pursue their primary education. Gamification and game-based learning methodologies have gained popularity in the global education sector in the recent past. The goal of this study is to investigate the present state of adopting gamification and game-based learning for primary education student cohorts, with the aid of recent peerreviewed research publications. By employing a systematic mapping design, reviewed paper results are categorized and further evaluated in relation to the attributes such as type of gamification, game mechanics and elements used, evaluation context, type of experimental outcome, gamified academic subject area, and type of application. With the aid of review results, this study proposes a mapping of gamification learning mechanics, suited to address Generation Alpha traits. It also recommends future research directions, which include investigating the realistic learning preferences of Generation Alpha, the need for designing novel educational game elements and adaptive gamification learning strategies, and the importance of long-term studies with diverse learner samples. The findings of this study will support prospective stakeholders, such as researchers, educators, parents, and educational policymakers, to utilize gamification approaches effectively in educating Generation Alpha. [ABSTRACT FROM AUTHOR]

Financial time series forecasting is crucial for making informed investment decisions. This study proposes an improvised method utilizing a Neuro-Fuzzy System (NFS) for enhanced forecasting accuracy. Traditional forecasting methods often struggle with the nonlinear and dynamic nature of financial time series data. NFS integrates neural network and fuzzy logic techniques, offering a robust framework for modeling complex relationships within financial data. The proposed method employs NFS to adaptively learn and model the intricate patterns present in financial time series data. It combines the strengths of neural networks in learning complex patterns and fuzzy logic in handling uncertainty and imprecision. This study contributes by introducing an innovative approach to financial time series forecasting, leveraging the capabilities of NFS to improve forecasting accuracy and reliability. Experimental results demonstrate the effectiveness of the proposed method in accurately forecasting financial time series data. The method outperforms traditional forecasting techniques, showcasing its potential for practical applications in financial markets. [ABSTRACT FROM AUTHOR]

This study uses the theory of planned behaviour to investigate how Indonesian Accounting educators responded to a shift to on-line learning during the height of the Covid-19 pandemic. The study looks specifically at the degree to which educators used this as an opportunity to implement pedagogies that move beyond a content orientation, providing an impetus for increased attention to adaptive/soft-skills elements the literature refers to as '21st century learning'. The findings indicate that attitudes toward adoption, subjective norms, and perceived behavioural control all played a significant role in determining Accounting educators' intentions to adopt twenty-first century learning pedagogical principles in Accounting online learning. Additionally, intention acts as a significant mediator of the indirect effect of these three variables on the adoption of twenty-first century learning in Accounting online learning. The concluding section lists limitations, practical implications, and suggestions for additional research. [ABSTRACT FROM AUTHOR]

Adaptive learning strategies applied to e-learning have become a relevant approach towards diversity and inclusion. They bring several benefits to learners in the role of digital platform users, related to the user experience, primarily in the learning process optimization. It aims to provide mediation, tailored content, and adequate channels for users' capabilities and learning styles. The OpenEdR4C is a digital open educational platform designed to expand complex reasoning skills in students and lifelong learners of higher education. The platform addresses five types of learning obstacles: sensory limitations, learning styles, sociodemographic and socioeconomic contexts, and certain kinds of neurodiversity. All these considerations require a dynamic and assertive user profiling strategy to provide compelling adaptive learning experiences. This paper presents a Systematic Literature Review of user profiling strategies published in the last five years in SCOPUS and Web of Science databases. The findings allowed us to identify successful and applicable strategies that the OpenEdR4C research and development teams might use to select and shape the suitable strategy for the platform in two levels: a) procedures that favor the user to self-declare their profile and preferences; and b) profiling based on the system's detection of patterns and behaviors shown by the users. The results and discussion presented are valuable insights for educators, developers in the context of open educational resources design, and decision-makers of HiEd institutions or training centers. There is a suitable strategy for every type of profiling necessity; here is a combination of many to be used and developed collectively. [ABSTRACT FROM AUTHOR]

The aim of this article is to explore improvisational handling of critical work practices during the COVID-19 pandemic and interpret these practices from a learning perspective. Based on an interview study with representatives of private, public and intermediary organisations, the study identified three different types of improvisational handling as responses to the pandemic crisis involving 'scaling up' and 'scaling down' critical work practices. By 'scaling up' and 'scaling down', we refer to practices for which, due to the pandemic, it has been imperative to urgently scale up an existing operational process or develop a new process, and alternatively extensively scale down or cease an existing process. The types of improvisational handling differed depending on the discretion of involved actors in terms of the extent to which the tasks, methods and/or results were given beforehand. These types of improvisational handling resulted in temporary solutions that may become permanent after the pandemic. The framework and model proposed in the article can be used as a tool to analyse and learn from the changes in work practices that have been set in motion during the pandemic. Such learning may improve the ability to cope with future extensive crises and other rapid change situations. [ABSTRACT FROM AUTHOR]

In the field of biology education, adaptive learning has been tested through a case study, serving as a crucible for innovative teaching methodologies designed to provide tailored, engaging, and effective learning experiences. This paper meticulously explores the implementation of select innovations in adaptive learning and reports on the outcomes derived from our fictitious case study. Our research underscores the efficacy of personalized learning pathways, where advanced algorithms dynamically customize content delivery based on individual students' needs and learning styles. Through our case study, we present compelling numerical evidence of a 25% improvement in learning outcomes and a notable 20% increase in student engagement. The strategic integration of real-time feedback and assessment mechanisms plays a pivotal role in enhancing the comprehension of biological concepts, as reflected in a 30% increase in student performance and a 15% boost in knowledge retention. Another simulated innovation involves the incorporation of gamification elements, resulting in a statistically significant 18% increase in student participation and a remarkable 22% surge in enthusiasm for biology studies. Our simulated findings accentuate how these elements can make biology education more enjoyable and motivating in a controlled environment. Furthermore, the simulated utilization of multimodal learning resources, such as videos, simulations, and interactive models, showcases a 28% improvement in students' ability to grasp complex biological concepts. This translates into a tangible 25% enhancement in student performance in assessments. The simulated investigation into the potential of AI-enhanced assistance, with AI chatbots and virtual tutors, reveals a simulated 35% increase in student satisfaction and a 27% improvement in performance. These simulated innovations demonstrate the positive impact of AI integration on student support. In addition, our simulated data-driven insights inform content and platform improvements, resulting in a simulated 20% increase in the adaptability of the learning system. These simulated results provide crucial insights into the optimization of adaptive learning in biology education. Through the presentation of these simulated innovations and their associated numerical results, this paper underscores the transformative potential of adaptive learning in the simulated context of biology education. These simulated innovations not only offer tailored learning experiences but also yield concrete, positive outcomes in terms of student understanding and performance. The simulated implications of our research are discussed in detail, emphasizing the promising future of these innovations in shaping simulated biology education. Furthermore, we suggest avenues for future research to continue improving adaptive learning methods, ensuring the simulated advancement of biology education in the digital age. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss the application of contemporary AI tools and technologies in education. The paper will mainly focus on a close examination of relevant academic literature as well as the current situation and methods of AI implementation in different sectors, finally, in education. Particularly, it was crucial that there were identified the major directions of applying AI in learning with an emphasis on learners and educators being the basic aspects of AI use within modern educational approaches. Secondly, some benefits and drawbacks concerning AI implementations in education supported by experiences from industry leaders and specific satisfaction measures among existing users, are analyzed. The most significant part covers integrating findings that show facts about artificial intelligence being used in education for 2024 like top online learning statistics for 2024, top AI statistics for 2024, and best AI plagiarism checkers for teachers in 2024 respectively. This includes evaluating teacher or instructor preparation skills to employ artificial intelligence (AI) tools and technology as well as ways students can learn how to use them without misuse towards quality education improvement.

This systematic review examines strategies for designing AI-enhanced language learning environments anchored in collaborative partnerships between humans and AI. The review involved searching multiple databases for relevant literature published between 2000-2023, applying inclusion/exclusion criteria, and coding articles according to a predefined scheme. A total of 10 studies were identified that addressed guidelines for structuring roles, coordinating AI with human priorities, assessing user perceptions, applying AI to personalized learning, or leveraging AI capabilities while maintaining central human involvement. Key findings indicate guidelines emphasize delineating roles between humans and AI through frameworks balancing autonomy and expertise. Techniques show potential for aligning AI with human input, though ensuring real-world coordination requires ongoing refinement. Research underscores generally positive user perceptions depending on individual attributes and initial adoption intentions. Personalized learning through AI modeling emerges as promising when guided by educators. Designing AI to enhance rather than replace teachers emphasizes collaborative problem-solving. Findings offer guidance on thoughtful AI integration respecting people as learning relationships evolve. Continued investigation refining coordinated approaches across contexts could help realize equitable AI-augmented models optimizing outcomes through empowered human partnerships as technologies progress. This provides direction for responsibly advancing the field to maximize AI's contributions to language education.

This paper presents an interdisciplinary framework, Machine Psychology, which integrates principles from operant learning psychology with a particular Artificial Intelligence model, the Non-Axiomatic Reasoning System (NARS), to advance Artificial General Intelligence (AGI) research. Central to this framework is the assumption that adaptation is fundamental to both biological and artificial intelligence, and can be understood using operant conditioning principles. The study evaluates this approach through three operant learning tasks using OpenNARS for Applications (ONA): simple discrimination, changing contingencies, and conditional discrimination tasks. In the simple discrimination task, NARS demonstrated rapid learning, achieving 100% correct responses during training and testing phases. The changing contingencies task illustrated NARS’s adaptability, as it successfully adjusted its behavior when task conditions were reversed. In the conditional discrimination task, NARS managed complex learning scenarios, achieving high accuracy by forming and utilizing complex hypotheses based on conditional cues. These results validate the use of operant conditioning as a framework for developing adaptive AGI systems. NARS’s ability to function under conditions of insufficient knowledge and resources, combined with its sensorimotor reasoning capabilities, positions it as a robust model for AGI. The Machine Psychology framework, by implementing aspects of natural intelligence such as continuous learning and goal-driven behavior, provides a scalable and flexible approach for real-world applications. Future research should explore using enhanced NARS systems, more advanced tasks and applying this framework to diverse, complex tasks to further advance the development of human-level AI.

ObjectiveIn this study, we examined whether vestibular migraine, as a source of increased perceptual uncertainty due to the associated dizziness, interferes with adaptive learning.MethodsThe IOWA gambling task (IGT) was used to assess adaptive learning in both healthy controls and patients with migraine-related dizziness. Participants were presented with four decks of cards (A, B, C, and D) and requested to select a card over 100 trials. Participants received a monetary reward or a penalty with equal probability when they selected a card. Card decks A and B (high-risk decks) involved high rewards (win £100) and high penalties (lose £250), whereas C and D (low-risk decks; favorable reward-to-punishment ratio) involved lower rewards (win £50) and penalties (lose £50). Task success required participants to decide (i.e., adaptively learn) through the feedback they received that C and D were the advantageous decks.ResultsThe study revealed that patients with vestibular migraine selected more high-risk cards than the control group. Chronic vestibular migraine patients showed delayed improvement in task performance than those with acute presentation. Only in acute vestibular migraine patients, we observed that impaired learning positively correlated with measures of dizzy symptoms.ConclusionThe findings of this study have clinical implications for how vestibular migraine can affect behavioural adaption in patients, either directly through altered perception or indirectly by impacting cognitive processes that can result in maladaptive behavior.