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This study explores Arab university faculty's views on fully AI-generated assignments and research papers submitted by students, what reasons they give for their stance and how they react in this case. Surveys with a sample of 45 Arab instructors revealed that 98% do not accept AI-generated assignments and research papers from students at all. They gave numerous reasons for their position. If students submit AI-generated assignments or research papers, they would ask them to re-write them. The study recommends raising students' awareness of university policies regarding AI-generated content and introducing faculty and students to AI plagiarism detection tools. Faculty views and recommendations are reported in detail.

There has been a policy push in K-12 educational settings towards personalized learning in the last decade. Commercial platforms and learning designers have responded, offering learning tools to support teaching and learning through data-driven insights and recommendations. Trending towards the augmentation or replacing human teachers with non-human technology, this paper argues that personalized learning with human teachers is an entirely different process from personalization with digital twins. Drawing on new materialist thinking, it explores the impacts and implications for discourse concerning teacher quality and disadvantages within educational systems. It clarifies the conflation of the terms "personalized learning" and "personalization" to illuminate the power, positionality, and privilege enabled for some, in conflating terms in Australian educational policy.

Artificial Intelligence in higher education (AIED) is becoming a more important research area with increasing developments and application of AI within the wider society. However, as yet AI based tools have not been widely adopted in higher education. As a result there is a lack of sound evidence available on the pedagogical impact of AI for learning and teaching. This conceptual paper thus seeks to bridge the gap and addresses the following question: is artificial intelligence really the new big thing that will revolutionise learning and teaching in higher education? Adopting the technological pedagogical content knowledge (TPACK) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical foundations, we argue that Artificial Intelligence (AI) technologies, at least in their current state of development, do not afford any real new advances for pedagogy in higher education. This is mainly because there does not seem to be valid evidence as to how the use of AI technologies and applications has helped students improve learning, and/or helped tutors make effective pedagogical changes. In addition, the pedagogical affordances of AI have not yet been clearly defined. The challenges that the higher education sector is currently experiencing relating to AI adoption are discussed at three hierarchical levels, namely national, institutional and personal levels. The paper ends with recommendations with regard to accelerating AI use in universities. This includes developing dedicated AI adoption strategies at the institutional level, updating the existing technology infrastructure and upskilling academic tutors for AI.

This study delves into the opportunities and challenges associated with the deployment of AI tools in the education sector. It systematically explores the potential benefits and risks inherent in utilizing these tools while specifically addressing the complexities of identifying and preventing academic dishonesty. Recognizing the ethical dimensions, the paper further outlines strategies that educational institutions can adopt to ensure the ethical and responsible use of AI tools. Emphasizing a proactive stance, the paper suggests that by implementing these strategies, schools can harness the benefits of AI tools while mitigating the risks associated with potential misuse. As the adoption of AI tools in education continues to expand, all stakeholders must stay abreast of the latest developments in the field. This knowledge equips educators to navigate the opportunities and challenges posed by AI tools, fostering a learning environment that is both secure and conducive to empowering students to realize their full potential.

Artificial Intelligence (AI) has been part of every citizen's life for several years. Still, the emergence of generative AI (GenAI), accessible to all, has raised discussions about the ethical issues they raise, particularly in education. GenAI tools generate content according to user requests, but are students using these tools ethically and safely? Can teachers guide students in this use and use these tools in their teaching activities? This paper argues that teacher professional development (TPD) is an essential key trigger in adopting these emerging technologies. The paper will present an integrative literature review that discusses the components of TPD that may empower teachers to guide their students towards the ethical and safe use of GenAI. According to the literature review, one key component of TPD should be AI literacy, which involves understanding AI, its capabilities and limitations, and its potential benefits and drawbacks in education. Another essential component is hands-on activities that engage teachers, their peers, and students in actively using these tools during the training process. The paper will discuss the advantages of working with GenAI tools and designing lesson plans to implement them critically in the classroom.

This paper explores recent advancements and implications of artificial intelligence (AI) technology, with a specific focus on Large Language Models (LLMs) like ChatGPT 3.5, within the realm of higher education. Through a comprehensive review of the academic literature, this paper highlights the unprecedented growth of these models and their widereaching impact across various sectors. The discussion sheds light on the complex issues and potential benefits presented by LLMs, providing a comprehensive overview of the field's current state. In the context of higher education, the paper explores the challenges and opportunities posed by LLMs. These include issues related to educational assessment, potential threats to academic integrity, privacy concerns, the propagation of misinformation, Equity, Diversity, and Inclusion (EDI) aspects, copyright concerns and inherent biases within the models. While these challenges are multifaceted and significant, the paper emphasises the availability of strategies to address them effectively and facilitate the successful adoption of LLMs in educational settings. Furthermore, the paper recognises the potential opportunities to transform higher education. It emphasises the need to update assessment policies, develop guidelines for staff and students, scaffold AI skills development, and find ways to leverage technology in the classroom. By proactively pursuing these steps, higher education institutions (HEIs) can harness the full potential of LLMs while managing their adoption responsibly. In conclusion, the paper urges HEIs to allocate appropriate resources to handle the adoption of LLMs effectively. This includes ensuring staff AI readiness and taking steps to modify their study programmes to align with the evolving educational landscape influenced by emerging technologies.

This position paper introduces the idea of cognicy, the foundational ability to think and understand in a process that decouples cognitive processes from their tangible outcomes. Generative artificial intelligence (AI) can produce output often nearly indistinguishable from a human product, which presents a problem for educational assessment. Cognicy focuses on the process of thought, which is uniquely human, rather than the output, which can be machine generated. The nearest parallel is numeracy, which decouples the underlying mathematical concept from the task of calculation. Similarly, cognicy seeks to disentangle the essential thought process from the outputs, which now can be easily composed by AI. Cognicy is thus a tool for shifting the way in which higher education views the intersection of generative artificial intelligence, learning, and evaluation. It must be where future frameworks for learning focus. Process must be seen as separate from product so that human skills and learning stay relevant. This paper gives a name to these human-based, AI adjacent skills, creating a shared language to begin larger discussions. As a means of starting the conversation, the paper explores the relationship of cognicy to the concepts of Universal Design for Learning (UDL), metacognition, and AI literacy to show how this emerging framework might be employed.

Still rebounding from the impact of the global pandemic, the higher education sector is being challenged even further by the next wave of Artificial Intelligence (AI) technologies. These technologies have the power to generate in a matter of seconds, quality text, images, music and coding responses to questions or prompts entered into an online chat box. Currently, one of the most accessible and popular text generators is OpenAI's ChatGPT which was released in November 2022. Early evaluation indicates that the quality of the responses exceed standard pass rates for comparable university assessments. Even if academic protocols mandate that text cited from AI sources should be acknowledged and referenced as any other source material, the speed, accessibility and high quality of the AI material justifies a rethink of the purpose of higher education and a redesign of curriculum, pedagogy and assessment. An initial suggestion being promoted in the sector is that learning outcomes and assessments should move away from a focus on content memorisation and recall, to development of higher order thinking skills such as critical analysis, evaluation, resilience, creativity, problem solving, appraising and mastery of verbal communication and computer literacy. This preliminary paper examines some of the literature to date, which discusses potential risks and threats, as well as the opportunities to enhance learning, embedded in this new wave of emerging AI technologies in higher education.

In this paper, we describe a teaching scenario using a virtual environment (known also in the context of the 'metaverse'). This is motivated by the challenges that arise during the pandemic. More and more teaching scenarios are transferred to online learning settings, which allow learning at any time and at any time. One of the possibilities are virtual 3D environment. These allow more intensive immersion than for example video conferences. Furthermore, they offer new didactic concepts, for example, for group activities. The benefit of using virtual 3D environments we demonstrate by a concrete learning scenario: the simulation of robot programming. A further advantage when using virtual 3D environments are personal assistants (conversational/ pedagogical agents), for example, to the ease the work load borne by teachers; meanwhile, this works well also with natural language due to advantage stage of artificial intelligence implementations.

In this paper we describe a framework for teaching students when they should, or should not use generative AI such as ChatGPT. Generative AI has created a fundamental shift in how students can complete their class assignments, and other tasks such as building resumes and creating cover letters, and we believe it is imperative that we teach students when the use of generative AI is appropriate, and when it is not appropriate (i.e., considered cheating). Framework development is based off the 2x2 Product-Market matrix introduced by Ansoff in 1965. Our initial pass at the framework was piloted with colleagues, and then followed with a focus group of students to refine the framework. We then used the framework in an MBA class to test its efficacy and gather qualitative feedback. Using the results, we further refined the framework and then used it to teach two general undergraduate business classes as a rudimentary test of generalizability across students. The qualitative results were positive. The framework helps educators understand when to use, or not use ChatGPT, and provides a way to teach students about the same. We have found that using the framework in class generates interesting discussions about the use of generative AI.

In an era of major advances in the digital world, artificial intelligence has been a part of programs, tools, applications, and platforms. It has also been integrated into fields of education including language teaching and learning. To this end, ChatGPT, one of the most recent AI-driven systems, has been proposed to support language learners' personalized studies. Thus, this paper presents a qualitative study aiming to explore how Turkish EFL learners in higher education use ChatGPT for speaking. For a deeper understanding, the study was designed as a case study which used multiple sources to collect qualitative data. In this sense, semi-structured interviews were held with the participants, and through open-ended questions, they were asked about their study habits and any background knowledge about ChatGPT. In a following session, they were introduced the chatbot and instructed on possible ways to use it for speaking practice. Screen recordings of the usage by the participants were another source to observe and later describe the process for the researcher. A final session of the interviews planned as a stimulated recall was held to explore the participants' ChatGPT use experience through their reflection. The thematic analysis of the data revealed codes and themes leading to language teaching implications about learner characteristics and use of AI for language studies.

The technology-supported education systems seamlessly integrated throughout the globe in response to the demands of post COVID-19 pandemic. The swift developments of the digital tools with Artificial Intelligence (AI) support are also readily diffused among the educational communities. This research paper investigates the synergistic impact of digital literacy, the incorporation of AI tools, and Peer Supported Collaborative Learning (PSCL) on the learning perceptions of university students. The research aims to discern the implications of these technological and social facets on students' attitudes towards AI assisted learning process. Structured questionnaire-based survey among the University students were done for this descriptive research. 409 responses collected were analysed with SPSS, Excel and Process Macro. It is found that the students' Digital Literacy, Use of AI tools and PSCL on AI assisted learning were positively correlated. The partial mediatory path through the PSCL and AI tool usage has a significant positive influence on students learning process. The insights gathered from this study can inform educators, policymakers, and institutions on optimizing the amalgamation of digital literacy, AI tools and PSCL to enhance the contemporary learning environment. As universities navigate the digital age, this research provides a nuanced understanding of the dynamics shaping students' perceptions, offering valuable insights into the multifaceted aspects of AI influencing the educational landscape.

This paper presents the case of an adjunct university professor to illustrate the dilemma of using artificial intelligence (AI) technology to grade student papers. The hypothetical case discusses the benefits of using a commercial AI service to grade student papers--including discretion, convenience, pedagogical merits of consistent feedback for students, and advances made in the field that yield high-quality work--all of which are achieved quickly. Arguments against using AI to grade student papers involve cost, privacy, legality, and ethics. The paper discusses career implications for faculty members in both situations and concludes with implications for researchers within the discourse on academic integrity.

The purpose of this study was to investigate the impact of mLearning or mobile device practice on the attention and accuracy of student's use of math concepts, specifically, telling time. A single subject, alternating treatment design was used to compare mLearning to paper and pencil practice in four 3rd grade male students. Results were mixed; two children were observed to be more on-task during the mLearning practice, and two children were observed to perform similarly across both conditions. Additionally, two children performed similarly on correctly completed problems across both conditions, and two children performed better using paper and pencil practice. All students completed more math problems during the paper and pencil practice.

Artificial intelligence tools have presented many challenges and opportunities to transform teaching and learning on college campuses. These changes are significant enough to require colleges to take action to create a framework by which faculty and students can navigate the proper usage of these tools. Rather than working to create entirely new policies strictly for addressing these new technologies, they should instead edit their existing academic integrity and intellectual property statements to incorporate explicit mentions of artificial intelligence.

In the context of education, the issues of integrating artificial intelligence (AI) into teaching and maintaining academic integrity in students' use of AI are particularly relevant. This paper empirically examined the issue of ChatGPT usage for writing homework from the perspectives of students and professors. Study research methods included both quantitative and qualitative approaches. In Study 1, an anonymous questionnaire was administered to 350 Croatian students, users of ChatGPT, to investigate their perceptions, attitudes, habits, and intentions regarding ChatGPT usage for homework assignments. In Study 2, twelve faculty members were tested on their accuracy of distinguishing between original students' papers and ChatGPT-generated papers. For this purpose, 25 different versions of papers for 8 different courses were prepared. The results of the students' survey showed that most students still do not use ChatGPT regularly and have neutral attitudes about its usefulness, ease of use, risks, and intentions for future use. In addition, they were moderately concerned about ethical issues around its usage. Differences across gender and field of study were found. Professors, on the other hand, reported having average self-efficacy in appraising authorship, which is in line with their low average accuracy of 53%. Accuracy in distinguishing was lowest when ChatGPT was instructed to write a paper as a student. These results strongly suggest the necessity for clear guidelines, plagiarism detection tools, and educational initiatives to promote ethical use of AI technology.

This paper considers the impact of technological processes on human thought, specifically the implications of artificial intelligence (AI) on writing instruction. The main purpose of this paper is to present instructional considerations that will elevate human voice and reduce student temptations to turn to AI unreasonably to produce a piece of writing while still providing responsible options for the incorporation of AI in the writing process.

Purpose: To align with the artificial intelligence and robotics (AIR) research and policy agenda, this paper puts forth an adapted five big ideas framework specifically tailored to teaching young children about artificial intelligence (AI) via robotics. Design/Approach/Methods: Grounded in early childhood education research, the proposed framework emphasizes the use of robotics and play-based learning to make AI accessible and encourage engagement among young children who have not started formal schooling. Findings: We comparatively analyze the commonalities and differences in AI big ideas between the original K-12 framework and the redeveloped early childhood education framework. To pique children's interest, key concepts are presented through interactions with robotics and robot role-play. This paper also provides recommendations for age-appropriate topics, storytelling, and play-based teaching methods. Originality/Value: This framework aims to equip researchers and educators with strategies for successfully integrating introductory AI education into early childhood classrooms. Teaching AI in a developmentally responsive manner can help nurture young children's curiosity toward and understanding of an increasingly AI-driven world.

This position paper explores the current state of artificial intelligence (AI) tools, educator support of and opposition to AI tools in teaching and learning, and the ethical and social implications of AI tools in higher education. As technology continuously develops in the educational community, educators must have a voice in how AI exists in the classroom. This paper addresses support of and opposition to AI implementation and the need for more studies on teacher and student experiences with AI tools, such as personalized learning platforms and intelligent tutoring systems. The practical applications of AI in future studies should explore how to best implement AI tools while advancing knowledge and maintaining academic integrity as students and faculty become more technologically literate citizens. Studies in educational technology have acknowledged that social and ethical implications arise from the advance of AI. Programming diverse practices into AI applications impact the data output when generating new content. Therefore, this position paper acknowledges the need for being inclusive in framing technology and AI tools across less developed countries, emerging economies, and developed countries using varying theories, such as situated learning theory, technology affordances and constraints theory, decolonial theory, and intersectionality.

This paper addresses the pedagogical implications of incorporating ChatGPT into the college English classroom specifically and, more broadly, into any college course with a focus on writing and research. Historically, advances in technology in the college classroom have characteristically promoted two juxtaposed reactions: relief and anxiety. Students customarily exhibit relief that a new technology will lessen their workload and embrace it wholeheartedly. Conversely, faculty often experience anxiety at how some newfangled computerized application will impact student learning. This juxtaposition creates barriers to an effective integration of new technology into the classroom. What students view as a cool new tool faculty see as a platform that promotes student slacking or, at worst, cheating. Such is the case with ChatGPT. I review generally the ethics of using ChatGPT as a classroom tool to conclude that the potential for advancing educational equity among students outweighs any potential for misuse of this quickly evolving technology. Relying upon established principles of classroom instruction as well as significant trial-and-error experience, I propose a pedagogical framework that allows for limited application of ChatGPT in selected scaffolded assignments. I further offer specific lesson plans to show how incorporation of ChatGPT into the college composition classroom can align with universally accepted goals, objectives, and student learning targets in both freshman composition and traditional literature courses, all while removing barriers and promoting equity. This paper provides faculty who are not already well-versed in ChatGPT with information to evaluate its efficacy for their courses and a flexible framework to include into their pedagogy easily modifiable ChatGPT-based lesson plans that present challenging yet fun scaffolded assignments for any writing or research curriculum.

In the realm of language acquisition, the integration of Artificial Intelligence (AI) presents a promising frontier. However, gaps exist in understanding the practical application of AI-driven tools, particularly in second language learning contexts. This study delves into the usability of ChatGPT, an advanced AI language model, within the domain of second language acquisition. This paper synthesizes existing literature on ChatGPT's multifaceted capabilities, its effectiveness, its associated challenges, and the potential solutions to these challenges in language learning environments. This review demonstrates ChatGPT's substantial potential in enhancing language learning outcomes, including fostering learner autonomy, improving motivation, and developing language proficiency. Yet, nuanced challenges such as preserving academic integrity and difficulties in crafting effective prompts emerge as crucial considerations. To address these issues, possible solutions including enhancing AI literacy among learners and educators are discussed. This paper sheds light on the complex dynamics of AI-assisted language education, urging ongoing research and refinement to fully utilize these technologies in enhancing second language learning.

This scoping review explores the ethical challenges of using ChatGPT in higher education. By reviewing recent academic articles in English, Chinese, and Japanese, we aimed to provide a deep dive review and identify gaps in the literature. Drawing on Arksey & O'Malley's (2005) scoping review framework, we defined search terms and identified relevant publications from four databases in the three target languages. The research results showed that the majority of the papers were discussion papers, but there was some early empirical work. The ethical issues highlighted in these works mainly concern academic integrity, assessment issues, and data protection. Given the rapid deployment of generative artificial intelligence, it is imperative for educators to conduct more empirical studies to develop sound ethical policies for its use.

Artificial Intelligence (AI) and related technologies have a long history of being used in education for motivating learners and enhancing learning. However, there have also been critiques for a too uncritical and naïve implementation of AI in education (AIED) and the potential misuse of the technology. With the release of the virtual assistant ChatGPT from OpenAI, many educators and stakeholders were both amazed and horrified by the potential consequences for education. One field with a potential high impact of ChatGPT is programming education in Computer Science (CS), where creating assessments has long been a challenging task due to the vast amount of programming solutions and support on the Internet. This now appears to have been made even more challenging with ChatGPT's ability to produce both complex and seemingly novel solutions to programming questions. With the support of data collected from interactions with ChatGPT during the spring semester of 2023, this position paper investigates the potential opportunities and threats of ChatGPT for programming education, guided by the question: What could the potential consequences of ChatGPT be for programming education? This paper applies a methodological approach inspired by analytic autoethnography to investigate, experiment, and understand a novel technology through personal experiences. Through this approach, the authors have documented their interactions with ChatGPT in field diaries during the spring semester of 2023. Topics for the questions have related to content and assessment in higher education programming courses. A total of 6 field diaries, with 82 interactions (1 interaction = 1 question + 1 answer) and additional reflection notes, have been collected and analysed with thematic analysis. The study finds that there are several opportunities and threats of ChatGPT for programming education. Some are to be expected, such as that the quality of the question and the details provided highly impact the quality of the answer. However, other findings were unexpected, such as that ChatGPT appears to be "lying" in some answers and to an extent passes the Turing test, although the intelligence of ChatGPT should be questioned. The conclusion of the study is that ChatGPT have potential for a significant impact on higher education programming courses, and probably on education in general. The technology seems to facilitate both cheating and enhanced learning. What will it be? Cheating or AI-enhanced learning? This will be decided by our actions now since the technology is already here and expanding fast. [Note: The publication year (2023) shown in the citation on the PDF is incorrect. The correct publication year is 2024.]

This paper provides a thorough examination of the role of Artificial Intelligence (AI), particularly ChatGPT and other AI language models, in the realm of education. Drawing insights from existing literature and a novel study on educator perspectives, the paper delves into the potential advantages, ethical dilemmas, and factors shaping educators' attitudes towards AI integration in education. AI language models have the potential to revolutionize educational content creation, personalize learning experiences, and streamline assessment and feedback processes. These capabilities hold the potential to enhance teaching and learning outcomes while catering to the diverse needs of students. However, ethical concerns loom large in the adoption of AI in education. Bias in generated content is a chief concern, as it can perpetuate societal biases and lead to unfair treatment or the dissemination of inaccurate information. The solution lies in rigorous data curation to ensure equitable educational experiences for all students. Moreover, the potential for generating inappropriate or misleading content poses a significant ethical challenge, impacting students' well-being, civic understanding, and social interactions. Safeguards must be implemented to detect and rectify biased or inappropriate content, fostering inclusive and unbiased learning environments. Transparency emerges as a crucial ethical consideration. The opacity of AI models like ChatGPT makes it difficult to comprehend their decision-making processes. Enhancing model interpretability and explainability is vital for accountability and addressing embedded ethical issues. Privacy concerns related to data collection and usage are emphasized in the literature. Clear policies and guidelines must govern data collection, use, and protection, ensuring data is solely employed for educational purposes and maintaining robust data security measures. Our study expands upon these insights by exploring socio-demographic factors, motivations, and social influences affecting educators' AI adoption in higher education. These findings inform institutions on tailoring AI integration strategies, emphasizing responsible usage through training, and assessing the impact on learning outcomes. As educational institutions increasingly embrace AI, including advanced models like GPT-4, a cautious and thoughtful approach is vital. Balancing potential benefits with ethical challenges ensures that AI enhances teaching and learning while upholding fairness, equity, and accountability. In summary, this paper illuminates the potential of AI in education, accentuates ethical concerns, and highlights the significance of understanding educators' perspectives. Collaboration between educators and policymakers is essential to navigate the complexities of AI integration, ensuring that education remains a realm of equitable, efficient, and accountable learning experiences.

This paper charts a forward-looking roadmap for open universities, drawing upon their historical evolution and current practices. It advocates a shift toward a universally accessible, personalized education system. At the heart of this proposed advancement lies the customization of learning paths and experiences, where individualized advising and mentorship, and a variety of learning content, resources, and environments are essential. The study underscores the importance of integrating advanced technologies such as artificial intelligence and blockchain into the open and distance education system. Within the discourse, the paper delineates three primary areas for open universities to address: system transformation, expansion of openness, and integration of digital innovation. The concluding part of the paper offers possible strategic recommendations for policymakers and researchers of open universities. The essence of these recommendations is advocating for a universally personalized educational paradigm while making a strong case for addressing the digital divide, fostering strong partnerships at both global and community levels, and supporting the use of the latest technology to its fullest potential. By navigating this transformative journey, open universities are not just participating in the evolution of educational models but also poised to lead a revolution in the broader landscape of higher education.

Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the implications of (1) a recent case related to the General Data Protection Regulation (GDPR) and (2) the release of ChatGPT, a generative AI model capable to producing 'human-like' text. These events raise questions for the future of AIEd and the underlying function of assessment, and highlight the importance of active student participation in the integration of AI in education. [This article has been presented in the 5th International Open & Distance Learning Conference-IODL 2022.]

The rapid advancement of artificial intelligence technologies, exemplified by systems including Open AI's ChatGPT, Microsoft's Bing AI, and Google's Bard (now Gemini 1.5Pro), present both challenges and opportunities for the academic world. Higher education institutions are at the forefront of preparing students for this evolving landscape. This paper examines the current state of AI education in universities, highlighting current obstacles and proposing avenues of exploration for researchers. This paper recommends a holistic approach to AI integration across disciplines, fostering industry collaborations and emphasizing the ethical and social implications of AI. Higher education institutions are positioned to shape an educational environment attuned to the twenty-first century, preparing students to be informed and ethical contributors in the AI-driven world.

Since the advent of computer-mediated communication (CMC) technologies, the landscape of education, especially in English Language Teaching and learning, has undergone significant transformations. Academic writing, a key skill in this context, has witnessed notable benefits from technological advancements, particularly in the contemporary era. One prominent technology reshaping the academic landscape is ChatGPT. While it offers abundant learning opportunities for second language learners and teachers, it also poses significant ethical challenges. This paper provides an overview of the opportunities and challenges presented by ChatGPT and other AI-based technologies concerning writing skills, specifically academic writing in English as a second language. The study includes a descriptive account of my interview with ChatGPT regarding the opportunities that it has presented and the challenges posed for L2 students and teachers. The interview results indicate that ChatGPT can be employed in various ways to enhance the second language writing process for both students and teachers, notwithstanding the latter's reservations about its ethical implications. In addition, the paper offers some practical activities that can be implemented in L2 academic writing classes.

Artificial intelligence (AI) is now affecting all aspects of our social lives. Without always knowing it, we interact daily with intelligent systems. They serve us invisibly. At least that is the goal we assign to them: to make our lives better, task by task. Artificial intelligence has the potential to make biology education more engaging, personalized, and effective by providing students with interactive simulations, personalized learning experiences, and other tools that help them understand complex biological concepts. In this paper, we discuss the integration of AI into the virtual classroom, which significantly enhances student learning experiences in various ways. The study shows that an effective integration of technology into the virtual classroom requires a thoughtful approach that aligns with educational goals and the specific needs of students. In fact, interactive simulations can help make biology more engaging and memorable for students. Besides, personalized learning AI algorithms can help biology students receive a more tailored and effective learning experience, helping them to better understand the course material and develop a deeper appreciation for the natural world. In this work, we will discuss the use of AI to enhance interactive simulation-based cellular processes, with additional application in anatomy, physiology, and ecology teaching. Moreover, this paper discusses how AI could be used to analyze student data and propose personalized learning using adaptive assessments, content recommendations, and data sciences. This paper illustrates examples of AI algorithms that could be useful for teaching biology.

The use of Artificial Intelligence (AI) in education is transforming various dimensions of the education system, such as instructional practices, assessment strategies, and administrative processes. It also plays an active role in the progression of science education. This systematic review attempts to render an inherent understanding of the evidence-based interaction between AI and science education. Specifically, this study offers a consolidated analysis of AI's impact on students' learning outcomes, contexts of its adoption, students' and teachers' perceptions about its use, and the challenges of its use within science education. The present study followed the PRISMA guidelines to review empirical papers published from 2014 to 2023. In total, 74 records met the eligibility for this systematic study. Previous research provides evidence of AI integration into a variety of fields in physical and natural sciences in many countries across the globe. The results revealed that AI-powered tools are integrated into science education to achieve various pedagogical benefits, including enhancing the learning environment, creating quizzes, assessing students' work, and predicting their academic performance. The findings from this paper have implications for teachers, educational administrators, and policymakers.

Research in languages and literacies in science education (LLSE) has developed substantial theoretical and pedagogical insights into how students learn science through language, discourse, and multimodal representations. At the same time, language is central to the functioning of generative artificial intelligence (GenAI). On this common basis concerning the role of language, this paper explores how foundational ideas from LLSE studies can inform the use of GenAI in science education. A bibliometric analysis of 412 journal articles from Web of Science provided the initial step to identify major themes and relationships in the LLSE literature. The analysis revealed four clusters of research in LLSE: reading and writing scientific text, science discourse and interaction, multilingual science classroom, and multimodality and representations. Each cluster was further analyzed through close reading of selected articles to identify and connect key constructs to the potential use of GenAI. These constructs include the interactive-constructive reading model, text genre, reading-writing integration, dialogic interaction, critical questioning, argumentation, translanguaging, hybridity, thematic pattern, modal affordance, and transduction. From these ideas and connections, the paper recommends several pedagogical principles for science educators to guide the use of GenAI. It concludes that LLSE research offers valuable insights for researchers and teachers to investigate and design the use of GenAI in science education. In turn, the impending use of GenAI also calls for a rethinking of literacy that will shape future research in LLSE.