Integration of artificial intelligence as a self-directed learning tool in an undergraduate physiology course.

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

Purpose The rapid integration of generative artificial intelligence (AI) tools, such as ChatGPT, into higher education presents both new opportunities and ethical challenges. This study aims to examine university students’ perceptions and ethical readiness toward the use of generative AI tools in the educational context, with a comparative focus on Science, Technology, Engineering, and Mathematics (STEM) and non-STEM disciplines. Design/methodology/approach The research uses the Ethical Adoption Model of Generative AI, an extension of the unified theory of acceptance and use of technology 2 (UTAUT2) framework that integrates ethical constructs, including ethical awareness (EA), perceived ethical risk (PER) and AI ethical anxiety (AIEA). Data were collected from 372 university students in Indonesia. Quantitative analyses, comprising t-tests, analysis of variance and partial least squares structural equation modeling (PLS-SEM), were used to examine both group differences and predictive relationships among constructs. Findings Results revealed significant differences between STEM and non-STEM students across several dimensions, particularly hedonic motivation, facilitating condition, social influence and AI ethical anxiety. Habit emerged as the most distinctive factor among STEM students, indicating deeper integration of AI into their academic routines. The PLS-SEM results identified performance expectancy, ethical awareness, habit and AI ethical anxiety as significant predictors of behavioral intention, with AI ethical anxiety exerting a negative influence. The findings underscore that STEM and non-STEM students experience distinct ethical concerns and adoption patterns, suggesting the need for differentiated institutional strategies, emphasizing technical empowerment for STEM students and ethical guidance for non-STEM students, to promote responsible and equitable AI adoption in higher education. Originality/value This study integrates UTAUT2 and ethical decision-making constructs to reveal an ethical mediation chain (Ethical Awareness → Perceived Ethical Risk → AI ethical anxiety) shaping students’ behavioral intention.

The integration of generative artificial intelligence (AI) tools like ChatGPT in education has raised concerns that students may become dependent on AI-generated solutions, potentially stifling the development of critical thinking skills. Compounding this issue is the fact that Bloom’s Taxonomy—the widely used framework for designing educational goals—fails to address the cognitive demands of AI-assisted learning. This exploratory study presents a revised framework that incorporates AI-specific competencies, offering a more relevant model for nurturing critical thinking in an AI-driven environment. Using a conceptual approach supported by empirical evidence from MSc Marketing students’ interactions with AI tools over 4 weeks, the study found that AI can both enhance and challenge critical thinking across cognitive, affective, and metacognitive domains. Key elements such as melioration, ethical reasoning, collaboration, and reflective thinking were identified as critical for developing deeper engagement with AI-generated content. The framework proposes 12 propositions that inform future research and pedagogical strategies. This study outlines a research agenda for examining AI’s impact on cognitive development, serving as a resource for educators, policymakers, and researchers seeking to adapt teaching methods for AI-assisted education.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptions of AI across various academic aspects, such as module outlines, learning outcomes, curriculum design, instructional activities, assessments, and feedback mechanisms. It evaluates the impact of AI on students' learning experiences, critical thinking, self-assessment, cognitive development, and academic integrity. This research used a structured survey distributed to 300 students through Microsoft Forms 365, yet the response rate was 29.67%. A structured survey and thematic analysis were employed to gather insights from 89 students. Thematic analysis is a qualitative method for identifying and analysing patterns or themes within data, providing insights into key ideas and trends. The limited response rate may be attributed to learners' cultural backgrounds, as not all students are interested in research or familiar with AI tools. The survey questions are about AI integration in different academic areas. Thematic analysis was used to identify patterns and themes within the data. Benefits such as enhanced critical thinking, timely feedback, and personalised learning experiences are prevalent. AI tools like Turnitin supported academic integrity, and platforms like ChatGPT and Grammarly were particularly valued for their utility in academic tasks. The study acknowledges limitations linked to the small sample size and a focus on undergraduate learners only. The findings suggest that AI can significantly improve educational experiences. AI provides tailored support and promotes ethical practices. This study recommends continued and expanded use of AI technologies in education while addressing potential implementation challenges.

This article explores the integration of artificial intelligence (AI) in an English course for Academic and Professional Communication, situated within the broader context of digital transformation in higher education and the evolving demands of academic integrity. Rather than restricting AI use, the study adopts a critical digital pedagogy approach to foster students' awareness of ethical and effective engagement with AI tools. The activities were designed to encourage reflective practices, promote critical analysis of AI-generated content, and establish clear guidelines for responsible use. These activities include setting explicit expectations for AI-assisted work, integrating reflective assessments to evaluate AI outputs, and designing tasks that strengthen students’ critical thinking and academic integrity. Additionally, the approach aims to improve students’ retention of the course content by encouraging them to use AI not as a shortcut, but as a means to actively reflect, process, and engage with the material explained in class. By interacting with AI to rephrase, question, edit, or elaborate on key concepts, students become more conscious of the content, reinforcing understanding and promoting deeper learning. The results suggest that the students developed a more nuanced understanding of the capabilities and limitations, enabling them to engage with it as a support tool rather than a substitute for the original work. The study concludes that structured integration of AI can enhance students' digital literacy, content retention, and critical thinking skills in the academic context.

This article overviews a study on integrating generative AI tools into education. English language education is critical for AI integration due to its reliance on text-based learning, communication skills and adaptive instructional approaches. Employing a systematic literature review to analyse existing research on AI tools in English language education, it focuses on their benefits and challenges. Articles from Scopus-indexed journals and other scholarly sources published between 2021 and 2024 were reviewed. The study discusses four significant findings: the classification of generative AI, key research topics on AI, the role of AI in cognitive offloading and academic dishonesty and the phenomenon of AI-generated hallucinations. The findings highlight the need for educators to stay updated with AI advancements and adapt their teaching practices accordingly. While AI offers opportunities for personalised learning, it also raises concerns about academic integrity and the reliability of AI-generated content. The review is limited to studies published between 2021 and 2024 and may not encompass all relevant research. Additionally, the focus is primarily on English language education, which may not fully represent the impact of AI tools across other educational contexts. Future research could explore the broader implications of AI integration across different subject areas and examine its long-term effects on pedagogy and student learning outcomes.

The integration of Artificial Intelligence (AI) tools into academic research has become increasingly prevalent among higher education students. This case study explores how pre-service science teachers at Penang Teachers’ Training Institute utilise AI tools during the development of research proposals in the "Fundamental Research in Science Education" course. A mixed-methods questionnaire was used to explore AI usage patterns, perceived benefits and challenges, and ethical practices. Findings revealed that all students (100%) used ChatGPT, with 53.8% using it daily and 84.6% integrating AI-generated content for less than half their proposals. The data reveals that 76.9% used AI primarily for first drafts, 69.2% for topic exploration, and 53.8% for grammar checking. Students reported high perceived benefits, with 84.6% agreeing that AI enhanced proposal quality, 76.9% cited time savings, and 76.9% found it helpful for idea generation. However, the major challenges emerged with 76.9% encountering inaccurate information and 15.4% noting AI's limited understanding of specialised scientific concepts. Students exhibited strong ethical awareness, with all participants disclosing AI use to instructors, demonstrating transparency. Although only 38.5% expressed full trust in AI outputs, 84.6% actively verified content using academic sources. Open-ended responses further highlighted AI’s role in refining language, generating ideas, and clarifying research focus. The study concludes that pre-service science teachers engage thoughtfully with AI tools as supplementary academic supports rather than replacements for critical thinking. It also highlights the importance of integrating AI literacy, ethical guidelines, and institutional support into teacher education programs to foster responsible and effective AI engagement in academic work.

Aim/Purpose: Evaluate teachers’ perceptions, strategies, and challenges in integrating artificial intelligence (AI) into K-12 education and identify patterns and trends in the data from the reviewed studies. Background: This systematic review examines a decade of innovation to explore the transformative impact of AI on education (2014–2024). Adhering to PRISMA 2020 guidelines, the study uncovers key trends, challenges, and breakthroughs in AI-driven teaching and learning, offering a comprehensive perspective on how AI reshapes educational practices and methodologies. Methodology: The study employs a systematic review to analyze the implementation of AI techniques and tools in primary education, following the PRISMA 2020 guidelines to ensure the reliability and effectiveness of the findings. To achieve this, an extensive search was conducted in academic databases such as Web of Science, Scopus, and ERIC, focusing on empirical studies and peer-reviewed articles published between 2014 and 2024. Only accessible, peer-reviewed articles classified under Education and Educational Research and published in English or Spanish were selected. The search strategy was structured into five categories aligned with the research questions to identify relevant studies accurately. The selection process was carried out in three phases – Identification, Screening, and Inclusion – applying predefined criteria to guarantee the quality and relevance of the selected studies. Of an initial total of 514,919 articles, 488,940 were excluded for not meeting the inclusion criteria. After removing duplicates and evaluating titles, abstracts, and full texts, a final set of 28 studies was included. Contribution: The study explores the integration of AI in primary education, revealing both teachers’ enthusiasm and the challenges they face. While AI is perceived as a tool to enhance critical thinking, problem-solving, and student engagement, its implementation is limited by insufficient training, resources, and institutional support. Despite these obstacles, teachers show confidence in designing AI-integrated curricula, though this is weakened by inadequate infrastructure and technical support, highlighting the need for continuous professional development. The study also stresses the importance of establishing a competency framework for AI literacy and adopting a systemic approach to AI education. Additionally, ensuring safe learning environments by addressing data privacy and AI biases remains a key challenge. Overcoming these issues is essential for the ethical and effective integration of AI, maximizing its benefits while safeguarding student equity and security. Findings: - Educators see the potential of AI to personalize learning. - Barriers are lack of training and resources for teachers. - Importance of continuous training in digital skills. - Need for policies that promote AI literacy. - Collaboration with experts to optimize AI in the classroom. Recommendations for Practitioners: Teachers are encouraged to collaborate in using AI tools to enhance educational outcomes, supported by continuous professional development programs, clear policies that safeguard privacy and promote equality, and a framework that preserves human autonomy in integrating AI technologies. Recommendation for Researchers: The lack of empirical research on AI interventions in education limits understanding of its true impact, highlighting the need for future studies to fill this gap and optimize its application for greater educational benefits. Impact on Society: The integration of AI in K-12 education is not just an opportunity; it is a necessity to prepare future generations for an increasingly digital world. While AI has the potential to revolutionize learning by fostering critical thinking, personalization, and engagement, its impact depends on how effectively it is implemented. To ensure its benefits, it is essential to empower educators and students with AI literacy, address issues like bias and data privacy, and establish robust legal frameworks for fair and transparent use. Without proactive policies, AI could widen educational inequalities instead of reducing them. A responsible, human-centered approach is needed to create an inclusive, ethical, and effective AI-powered education system. Future Research: The article highlights the urgency of future empirical research to better understand the real impact of AI in education, as the lack of intervention studies limits its optimal application. Analyzing how AI influences learning outcomes, teaching dynamics, equity, and accessibility is essential, along with investigating the pedagogical competencies and technological conditions that affect its adoption. To this end, expanding the scope of studies is recommended by incorporating multicultural and multilingual perspectives, exploring AI applications across various disciplines and educational levels, and promoting interdisciplinary approaches that address ethical, social, and pedagogical dimensions.

Purpose – This study investigated Slovenian tourism students’ perspectives on using artificial intelligence (AI) tools in Language for Specific Purposes (LSP) courses to uncover the extent of AI adoption, perceived benefits, and associated challenges. Methodology – An anonymous online survey was conducted with 387 students from two major Slovenian universities, capturing quantitative and qualitative data on usage patterns, familiarity, and attitudes towards AI integration in LSP. Findings – Descriptive analysis revealed that two-thirds of respondents occasionally use AI tools for language learning, with ChatGPT being the most popular (used by 90%), followed by Grammarly (30%) and AI Writer (10%). Students primarily leveraged these tools to prepare assignments, refine written expression, correct grammatical errors, and acquire new skills. While appreciating the time-saving benefits of AI tools, concerns about discouraging independent thinking and relying on potentially inaccurate data were prevalent. Contribution – This research provides valuable insights into the evolving role of AI in modern pedagogy, informing educators on responsible integration while fostering critical thinking and ethical awareness, thereby contributing to understanding how AI tools reshape educational practices and highlighting their potential to transform education while addressing key concerns.

The integration of Artificial Intelligence (AI) tools has reshaped higher education by offering adaptive and personalized learning experiences. With applications such as intelligent tutoring systems, adaptive platforms, and generative tools like ChatGPT and Grammarly, university students increasingly rely on AI to support academic tasks. While global studies highlight AI’s potential in enhancing critical thinking, problem-solving, and academic performance, evidence from Pakistan remains limited. This study examined the relationship between AI tool usage, critical thinking, problem-solving skills, and academic performance among 250 university students in Sialkot through a quantitative correlational design. Using standardized instruments, findings revealed a significant positive relationship between the use of AI tools and student’s critical thinking and problem-solving skills. Moreover, AI usage showed a notable impact on academic performance, highlighting its role as a supportive learning resource. Age and gender based variations indicating demographic influences were examined but revealed no significant differences in outcomes. The study provides context-specific insights into AI’s role in Pakistani higher education, emphasizing opportunities for improved learning alongside the need for responsible integration to sustain independent cognitive development.

In recent years, the integration of Artificial Intelligence (AI) in education has shown transformative potential across various educational levels, from primary school to university. This paper examines the multifaceted applications of AI in education, highlighting its role in enhancing teaching and learning experiences, as well as personalizing educational content. The aim of this paper is to shed light on the implementation of various AI tools in primary, secondary and tertiary education through practical examples gained through action research. At the primary school level, AI tools are employed to create engaging and adaptive learning environments, catering to the diverse needs and learning paces of young students. In secondary education, AI facilitates the development of critical thinking and problem-solving skills through interactive and personalized learning platforms. At the university level, AI is revolutionizing research methodologies, providing sophisticated data analysis tools, and supporting the creation of innovative learning management systems. Additionally, the paper highlights the challenges and ethical considerations associated with AI integration in education, such as data privacy, algorithmic bias, and especially the need for teacher training in AI literacy. Conclusively, we provide an overview of the benefits and limitations of AI in education, offering insights into future trends and implications for educational stakeholders.

The integration of artificial intelligence (AI) technologies into education has allowed students to use selected AI tools in research work. However, along with its obvious advantages, the ability of generative AI to hallucinate raises questions regarding the effectiveness of its use for a variety of research tasks. In this paper, the authors a) review the regulatory and legal basis for the use of generative AI tools in research work in the preparation of texts of conference presentations, academic articles, term papers and qualification works; b) review pedagogical studies dedicated to describing the experience of using generative AI tools in solving research problems; c) propose the distribution of functions between a research supervisor, artificial intelligence and a student/researcher in the triad “teacher — artificial intelligence — student”. Generative AI tools can take over many functions that have traditionally been performed by teachers and research supervisors, as well as by young researchers. These include developing a research work plan, searching for research sources, conducting a literature review, writing an abstract, etc. At the same time, the authors claim that at the present stage it is reasonable to talk about a joint solution of a number of the above-mentioned research tasks by supervisors and researchers, using generative AI tools as an assistant, the feedback from which should be subjected to critical reflection and verification. By transferring some of the functions to generative AI, the teacher/ research supervisor is not excluded from the educational process and management of the student’s research work. Their functions are modified and supplemented with new ones to teach students how to interact with AI tools, correctly formulate prompts, critically evaluate the received feedback and take full responsibility for the process and the result of work with generative AI.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

The integration of artificial intelligence (AI) tools into postgraduate supervision in higher education has accelerated globally, offering opportunities to enhance efficiency in research processes and academic mentoring. However, limited empirical evidence exists regarding the risks and challenges of this integration, particularly within Global South contexts such as South Africa. This study investigates the challenges associated with the use of AI tools in postgraduate supervision from a South African perspective. Anchored in a constructivist paradigm, the study adopts a qualitative research design, employing semi-structured interviews with 20 purposively selected participants—10 postgraduate students and 10 supervisors from faculties that are actively integrating AI into supervisory practices. Data were analysed thematically using qualitative content analysis. The findings identify six key challenges: increasing dependence on AI that may erode students’ critical thinking and originality; insufficient digital literacy and institutional support; financial and sustainability constraints; the questionable reliability and accuracy of AI-generated outputs; ethical dilemmas and limited cultural contextualisation; and resistance to technological change among supervisors. While acknowledging the potential of AI to enhance research productivity and the quality of supervision, the study cautions against its uncritical adoption, which may compromise academic integrity, creativity, and equity. It recommends institutional strategies, including subsidised AI access, structured training on ethical and critical AI use, the embedding of digital literacy in postgraduate curricula, and the fostering of collaboration with AI developers to ensure culturally relevant systems. A context-sensitive approach is essential to balance the affordances of AI with the preservation of human intellectual agency and critical scholarly engagement in postgraduate supervision.