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Effective integration of technology is critical for online, blended, hybrid, and face-to-face learning. The purpose of this paper is to introduce the SETI (Socio-Ecological Technology Integration) framework and explain what it adds to the scholarly research regarding effective technology integration. The SETI framework goes beyond previous frameworks and applies a holistic systems approach to understanding effective technology integration. This takes into account all aspects of technology integration including new findings uncovered during the COVID-19 lockdown. TPACK and SAMR were analyzed identifying their benefits and limitations and then the SETI framework was explained providing insight into the value of a whole systems approach to technology integration.

Geometry and spatial reasoning form the foundations of learning in mathematics. However, geometry is a subject often ignored by curriculum writers and teachers until high school, leading to students lacking in critical skills in geometric reasoning. As the United States moves into a new curriculum epoch, heralding the commencement of the national common core standards (CCS), one could question if CCS in geometry align with the essential understandings children need to be successful geometric thinkers. This paper begins with an examination of the essential understandings of geometric reasoning leading to an interpretation and critique of the elementary geometry CCS. Finally, the instructional implications are discussed, considering the common core progression through what we know about how children learn geometry.

English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. However, English learners face many challenges in gaining English language skills. Extant studies show that AI has affordances to support in English language teaching and learning ELT/L. This study answers the call to examine specific challenges and affordances for using AI in ELT/L. A systematic review method was used with PRISMA principles to identify 42 studies. Findings reveal the geographical locations of studies, learner ages and years of study. Grounded coding was then used to identify affordances of the use of AI in ELT/L in the areas of speaking, writing, reading, pedagogy and self-regulation. AI in ELT/L challenges uncovered were technology breakdowns, limited capabilities, fear and standardising language. Policymakers, funders, practitioners and educational leaders can use the information provided in this study to gain a holistic understanding of the current trend in the use of AI in ELT/L, and practical implications are provided to guide future use of AI.

Studies investigating autistic community research priorities indicate a mismatch between what autism research focuses on and what autistic people want to see researched. Furthermore, there has not been a research priority-setting exercise specifically with autistic people in Scotland, where there are unique cultural, political and social contexts. Using a community-based participatory design, we aimed to identify the research priorities of autistic adults living in Scotland. Autistic and non-autistic researchers designed and conducted a survey where 225 autistic adults rated and ranked research topics in order of importance and provided qualitative feedback on issues and questions important to them. The top five research priorities were mental health/well-being, identification and diagnosis of autistic people, support services, knowledge and attitudes towards autistic people and issues impacting autistic women. There were differences in priorities according to different intersections of identity, and qualitative responses indicated a desire for research to focus on support and understanding. The bottom three priorities concerned genetics, treatments and interventions and causes. These findings emphasise the need to address the gap between what autism research focuses on and the everyday lives of autistic people.

Technology has become commonplace in K-12 classrooms with students actively using digital technology to support learning. The purpose of this study was to investigate whether there is empirical support that implementing the ISTE Standards for Students can result in learning gains. A scoping review methodology was used to map extant literature to evidence of learning gains. To ensure transparency in the selection of the articles and the evidence gathering procedure, an a priori method was used. This study is unique in that it provides the first examination of a set of student standards for technology use to learning gains within empirical articles. The findings reveal that there is evidence that all seven of the standards can lead to learning gains. This study is helpful in providing direction and guidance for all, such as educational leaders, teachers, administrators, researchers and policy makers who are looking for a roadmap for effective technology integration that leads to student success.

ChatGPT, an AI chatbot developed by OpenAI, was released in November 2022, sparking a significant surge in global awareness and utilization of generative AI across various domains. Although recent studies have acknowledged the significance of ChatGPT in the education sector, they have yet to focus on exploring faculty attitudes toward ChatGPT. We gathered a comprehensive corpus of tweets containing "#ChatGPT" and "#highered" between November 30th, 2022, and April 30th, 2023. We analyzed data by triangulating VADER, NRC lexicon, and ground coding. Findings suggest that 40% of the expressed sentiments were positive, 51% were neutral, and 9% were negative. The study also revealed the diverse range of emotions held by higher education faculty regarding ChatGPT, with trust and joy being the most prevalent positive sentiments and fear and anger being the most prevalent negative sentiments. This study shed light on faculty members' perceptions of ChatGPT, contributing to a better understanding of the impact, emotions, and incorporation of ChatGPT in the higher education sector.

While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define human vs AI contribution to education because AI can accomplish more and more educational activities that used to be the prerogative of human educators. Therefore, it is imperative to rethink the respective roles of technology and human educators in education with a future-oriented mindset.

BackgroundmTORC1 activity is dependent on the presence of micronutrients, including Asparagine (Asn), to promote anabolic cell signaling in many cancers. We hypothesized that targeting Asn metabolism would inhibit tumor growth by reducing mTORC1 activity in well-differentiated (WD)/dedifferentiated (DD) liposarcoma (LPS).MethodsHuman tumor metabolomic analysis was utilized to compare abundance of Asn in WD vs. DD LPS. Gene set enrichment analysis (GSEA) compared relative expression among metabolic pathways upregulated in DD vs. WD LPS. Proliferation assays were performed for LPS cell lines and organoid models by using the combination treatment of electron transport chain (ETC) inhibitors with Asn-free media. 13C-Glucose-labeling metabolomics evaluated the effects of combination treatment on nucleotide synthesis. Murine xenograft models were used to assess the effects of ETC inhibition combined with PEGylated L-Asparaginase (PEG-Asnase) on tumor growth and mTORC1 signaling.ResultsAsn was enriched in DD LPS compared to WD LPS. GSEA indicated that mTORC1 signaling was upregulated in DD LPS. Within available LPS cell lines and organoid models, the combination of ETC inhibition with Asn-free media resulted in reduced cell proliferation. Combination treatment inhibited nucleotide synthesis and promoted cell cycle arrest. In vivo, the combination of ETC inhibition with PEG-Asnase restricted tumor growth.ConclusionsAsn enrichment and mTORC1 upregulation are important factors contributing to WD/DD LPS tumor progression. Effective targeting strategies require limiting access to extracellular Asn and inhibition of de novo synthesis mechanisms. The combination of PEG-Asnase with ETC inhibition is an effective therapy to restrict tumor growth in WD/DD LPS.

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lins concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Agricultural water management is increasingly prioritized throughout the world as producers are tasked with meeting growing crop demand while also managing environmental resources more sustainably. Likewise, agriculture is increasingly modifying the terrestrial water cycle. In response to these dynamics, the informal research discipline of agrohydrology continues to grow, fueled by a new era of rapidly evolving research tools and big data availability. While many researchers are actively invested in agrohydrology as a research topic, there remains a gap in formalizing this valuable discipline. This article aims to: (a) identify key research themes in agrohydrology, (b) conceptualize future research topics within each theme, and (c) estimate a timeframe before topics become pressing (i.e., before a topic becomes a limiting factor in advancing water management in an agricultural context). This commentary is meant to guide the trajectory of an evolving discipline of agrohydrology, the practice of agricultural water management at multiple nested scales, and the conversation of the invested public.

Abstract Malaria is a major public health problem, but many of the factors underlying the pathogenesis of this disease are not well understood, including protection from the development of febrile symptoms, which is observed in individuals residing in areas with moderate-to-high transmission by early adolescence. Here, we demonstrate that susceptibility to febrile malaria following Plasmodium falciparum infection is associated with the composition of the gut microbiome prior to the malaria season in 10-year-old Malian children, but not in younger children. Gnotobiotic mice colonized with the fecal samples of malaria-susceptible children were shown to have a significantly higher parasite burden following Plasmodium infection compared to gnotobiotic mice colonized with the fecal samples of malaria-resistant children. The fecal microbiome of the susceptible children was determined to be enriched for bacteria associated with inflammation, mucin degradation and gut permeability, and to have increased levels of nitric oxide-derived DNA adducts and lower levels of mucus phospholipids compared to the resistant children. Overall, these results indicate that the composition of the gut microbiome is associated with the prospective risk of febrile malaria in Malian children and suggest that modulation of the gut microbiome could decrease malaria morbidity in endemic areas.

Abstract Persistence of malaria parasites in asymptomatic hosts is crucial in areas of seasonally-interrupted transmission, where P. falciparum bridges wet seasons months apart. During the dry season, infected erythrocytes exhibit extended circulation with reduced cytoadherence, increasing the risk of splenic clearance of infected cells and hindering parasitaemia increase. However, what determines parasite persistence for long periods of time remains unknown. Here, we investigated whether seasonality affects plasma composition so that P. falciparum can detect and adjust to changing serological cues; or if alternatively, parasite infection length dictates clinical presentation and persistency. Data from Malian children exposed to alternating ~6-month wet and dry seasons show that plasma composition is unrelated to time of year in non-infected children, and that carrying P. falciparum only minimally affects plasma constitution in asymptomatic hosts. Parasites persisting in the blood of asymptomatic children from the dry into the ensuing wet season rarely if ever appeared to cause malaria in their hosts as seasons changed. In vitro culture in the presence of plasma collected in the dry or the wet seasons did not affect parasite development, replication or host-cell remodelling. The absence of a parasite-encoded sensing mechanism was further supported by the observation of similar features in P. falciparum persisting asymptomatically in the dry season and parasites in age- and sex-matched asymptomatic children in the wet season. Conversely, we show that P. falciparum clones transmitted early in the wet season had lower chance of surviving until the end of the following dry season, contrasting with a higher likelihood of survival of clones transmitted towards the end of the wet season, allowing for the re-initiation of transmission. We propose that the decreased virulence observed in persisting parasites during the dry season is not due to the parasites sensing ability, nor is it linked to a decreased capacity for parasite replication but rather a consequence decreased cytoadhesion associated with infection length.

ABSTRACT: Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called “sniffer technique”) and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 (“best model”), where all significant traits were included; model 2 (“on-farm model”), where DMI was excluded; and model 3 (“reduced on-farm model”), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (−0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

Artificial Intelligence in Education (AIEd) has experienced a rapid rise in the past decade. This systematic review is the first examining the use of AIEd in K-12 including 169 extant studies from 2011 to 2021. This study provides contextual information from the research, such as the educational disciplines, educational levels, research purposes, methodologies, year published and who the AI was intended to support. The grounded coding revealed affordances fit into three main themes of AIEd connecting to pedagogies (e.g., gaming, personalization), administration (e.g., diagnostic tools), and subject content. Challenges in AIEd K-12 included issues toward negative perceptions, lack of student and teacher technology skills, ethical concerns, and issues directly with the ease of use and design of the AI tools.

Purpose: This systematic review paper aims to examine extant empirical research involving educational technology during COVID-19 to provide an aggregated analysis of how the pandemic has influenced educational technology research. Design/methodology/approach: Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic review and an integrative review methodology, 50 primary research studies were selected from ten top-ranked educational research journals. These studies were reviewed regarding research purposes, methodologies, instruments, educational level, geographical distribution, and findings of the studies. Findings: The findings reveal four emerging themes--influencing factors, effectiveness, challenges and teachers. The majority of the studies focused on higher education. Quantitative research design based on a questionnaire was the most adopted method of investigation by researchers. Research limitations/implications: Search parameters focused on the top 10 journals in the field of educational technology. Although this provides a level of quality, it narrowed the search. Practical implications: For practitioners and researchers, this study provides a summary of the field to better understand what knowledge we have gained on the use of educational technology to enable a more agile, knowledgeable response to education in future emergencies. Originality/value: This systematic review is unique in examining how the pandemic has influenced educational technology research. It also provides insight into gaps in the research that future researchers can use as a springboard to enable a more knowledge and a more agile approach to future emergencies.

The implementation of virtual reality (VR) has gained popularity in the organizational settings in responding to the digital transformation in the era of Industry 4.0. VR offers immersive and authentic experiences for learners through simulated real-life scenarios during training. While the affordances have been widely discussed, there is a need to take a systems thinking approach and look at the challenges and side effects associated with the implementation. This systematic review is an effort to comprehensively understand the challenges and side effects along with the exploration of contexts, affordances, and attitudes toward VR applications of the VR initiative for training in organizational contexts. Following a ground theory approach, we analyzed 50 articles published since 2011 that covered a wide range of industries and locations worldwide. The findings from our analysis revealed multiple affordances, challenges, and side effects of applying VR in training. We hope to promote a discussion on VR integration, especially to caution researchers and practitioners the challenges and side effects of this initiative in the design and implementation process, to derive maximum value of the immersive technologies in learning. The results contribute to a comprehensive understanding of applying VR in organizational settings and informs design considerations for successful VR implementation in training programs.

ChatGPT was released to the public in November 30, 2022. This study examines how ChatGPT can be used by educators and students to promote learning and what are the challenges and limitations. This study is unique in providing one of the first systematic reviews using peer review studies to provide an early examination of the field. Using PRISMA principles, 44 articles were selected for review. Grounded coding was then used to reveal trends in the data. The findings show that educators can use ChatGPT for teaching support, task automation, and professional development. These were further delineated further by axial sub codes. Eight student uses were 24/7 support, explain difficult concepts, conversational partner, personalized feedback and materials, provide writing support, offer self-assessment, facilitate engagement, and self-determination. In addition to be affordances of the AI, the data from the articles also showed limitations to ChatGPT and misuses, specifically, inaccuracies and hallucinations, potential bias, and tool limitations. Misuses are plagiarism and cheating, privacy issues and spread of false information. This study is a springboard for researchers, practitioners, policy makers and funders in understanding the emerging state of the field of ChatGPT.

As online learners are increasingly diverse, making online learning more inclusive becomes an urgent need. Universal design has been perceived as a viable framework to reach the widest possible range of learners. However, applying universal design to online education has been a challenge to instructors due to different barriers. It is also unclear what evidence-based strategies can be infused into online education. In this systematic review, we selected and analyzed relevant studies and synthesized their designs and findings to explore the trends, strategies, impacts, challenges, and instructors' and students' attitudes toward applying universal design in online environments. We discuss implications for research and practice.

Abstract: In November 2021, the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop was held, which involved hundreds of researchers from dozens of institutions. There were 17 sessions held at the workshop, including one on ecohydrology. The ecohydrology session included various breakout rooms that addressed specific topics, including 1) soils and belowground areas; 2) watersheds; 3) hydrology; 4) ecophysiology and plant hydraulics; 5) ecology; 6) extremes, disturbance and fire, and land-use and land-cover change; and 7) uncertainty quantification methods and techniques. In this paper, we investigate and report on the potential application of artificial intelligence and machine learning in ecohydrology, highlight outcomes of the ecohydrology session at the AI4ESP workshop, and provide visionary perspectives for future research in this area.