The impact of generative AI on critical thinking skills: a systematic review, conceptual framework and future research directions

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

With the increasing use of Generative Artificial Intelligence (AI) tools like ChatGPT and Bard, universities face challenges in maintaining academic integrity. This research investigates the impact of these tools on learning outcomes (factual knowledge, comprehension, critical thinking) in selected universities of Ghana's Upper East Region during the 2023-2024 academic year. The study specifically analyzes changes in student comprehension and academic integrity concerns when using Generative AI for content generation, research assistance, and summarizing complex topics. A mixed-methods approach was employed, combining qualitative data from interviews and open-ended questions with quantitative analysis of survey data and academic records. The research focuses on three institutions: C. K. Tedam University of Technology and Applied Sciences, Bolgatanga Technical University, and Regentropfen University College. A purposive sampling technique recruited 150 participants (50 from each university) who had used Generative AI tools. Key findings show that 72% of students reported improved understanding of course material through Generative AI use, yet 75% cited academic integrity as a primary concern. Quantitative analysis revealed a weak to moderate positive correlation (r = 0.45) between AI tool usage and improved grades, with variations depending on the specific AI tasks performed. Qualitative data highlighted concerns about overreliance on AI and its impact on critical thinking skills. This research contributes to the ongoing debate on AI's role in education by providing valuable insights for educators and policymakers worldwide. The findings suggest that while AI tools can enhance comprehension, ethical considerations and potential drawbacks related to critical thinking require careful attention. The study concludes with recommendations for integrating AI literacy programs, developing ethical guidelines, and implementing advanced plagiarism detection systems to harness the benefits of Generative AI while mitigating risks to academic integrity. Although specific to the Upper East Region of Ghana, these insights may be applicable to other educational systems with similar characteristics.

Critical Thinking (CT) is recognized as a foundational competency for professional readiness, innovation, and ethical reasoning in higher education, enabling students to analyze information, evaluate evidence, and make reasoned decisions in complex environments. The rapid integration of Generative Artificial Intelligence (GenAI) tools, such as large language models, presents new opportunities and risks for CT development. This study conducts a systematic literature review to synthesize empirical evidence on the pedagogical implications and cognitive impact of GenAI on students’ CT. Following PRISMA guidelines, and search terms around GenAI Tools, Critical Thinking And Higher Education, on five major education research databases—Web of Science; Scopus; EBSCOhost (Education Source, ERIC, and APA PsycInfo); and Compendex and Inspec (Elsevier)—63 empirical studies published between January 2023 and April 2025 were analyzed across higher education contexts, disciplines, and intervention designs. Results indicate that GenAI offers notable cognitive affordances, including scaffolding reflective reasoning, promoting self-regulation, and facilitating iterative dialogue and argument evaluation. Pedagogical strategies clustered into four primary integration typologies: AI-based feedback prompts, dialogue simulation and reflection, AI-supported peer review, and critical engagement with AI-generated content. Nearly half of the studies reported statistically significant CT improvements, particularly when GenAI use was guided by structured prompts, reflective activities, and performance-based assessment. However, multiple risks persist, including cognitive offloading, uncritical acceptance of AI outputs, and diminished intellectual autonomy, especially in unguided or surface-level usage. This review highlights the need for intentional pedagogical design, validated CT assessment tools, and longitudinal studies to ensure GenAI acts as a catalyst rather than a substitute for human reasoning. By identifying effective integration strategies and outlining potential pitfalls, this study provides evidence-informed guidance for educators and institutions aiming to responsibly leverage GenAI to strengthen students’ CT skills.

Purpose This meta-analysis aims to comprehensively review the impact of Generative Artificial Intelligence (Gen-AI) on college students’ critical thinking (CT) by quantitatively integrating the results of relevant empirical studies to obtain the overall effect. Design/methodology/approach This meta-analysis synthesized data from 39 empirical studies published between 2023 and 2025. Effect sizes were calculated using random-effects models, and moderator analyses were conducted to examine potential influencing factors, including Gen-AI literacy level, disciplines, knowledge types, pedagogical approaches, user roles, Gen-AI interface types, Gen-AI roles, and Gen-AI task types. Findings The results indicated that Gen-AI had a moderately positive effect on CT (g = 0.591). Further analysis identified five significant moderating variables: disciplines, knowledge types, pedagogical approaches, Gen-AI roles and Gen-AI task types. Specifically, Gen-AI has the greatest positive impact on college students’ CT in STEM, procedural knowledge, inquiry-based learning, as a peer, and in the context of performing reflective and metacognitive tasks. These results suggest that within the overall contribution range of Gen-AI to college students’ CT, in some cases they may be more effective. Originality/value Previous research reviews, when exploring students’ higher-order thinking, did not make a clear distinction among the different types of thinking within them. Therefore, it is necessary to separate CT from broad learning outcomes or higher-order thinking and analyze its relationship with Gen-AI separately.

Despite the increasing number of studies indicating that generative artificial intelligence is conducive to cultivating college students’ critical thinking skills, research on the impact of college students’ use of generative artificial intelligence on their critical thinking skills in an open learning environment is still scarce. This study aims to investigate whether the use of generative artificial intelligence by college students in an open learning environment can effectively enhance their critical thinking skills. The study is centered around the following questions: Does the use of generative artificial intelligence in an open learning environment enhance college students’ critical thinking skills (what)? What is the mechanism by which the use of generative artificial intelligence affects college students’ critical thinking (how)? From the perspective of self-regulated learning theory and learning motivation theory, what are the reasons for the impact of generative artificial intelligence on college students’ critical thinking skills (why)? To this end, the study employs questionnaires and interviews to collect data. The questionnaire data are subjected to descriptive statistical analysis, correlation analysis, multiple stepwise regression analysis, and mediation effect analysis. Based on the analysis of interview materials and survey questionnaire data, the study reveals the impacts and mechanisms of college students’ use of generative artificial intelligence tools on their critical thinking skills. The findings of the study are as follows. First, the frequency of artificial intelligence use is unrelated to critical thinking skills, but using it for reflective thinking helps to develop critical thinking skills. Second, students with strong self-regulated learning skills are more likely to use generative artificial intelligence for reflective thinking and achieve better development in critical thinking skills. Third, students with strong intrinsic learning motivation are more likely to use generative artificial intelligence for reflective thinking and achieve better development in critical thinking skills. Consequently, the article analyzes the reasons from the perspectives of self-regulated learning theory and learning motivation theory and offers insights into how to properly use generative artificial intelligence to promote the development of critical thinking skills from the perspectives of higher education institutions, college teachers, and college students.

In this study, the effects of using generative artificial intelligence (GenAI) as a metacognitive scaffolding tool in flipped classrooms (FC) on students’ motivation, critical thinking(CT), self-efficacy, engagement, problem-solving, metacognitive strategy use, and learning flexibility were examined through an experimental design. The study was conducted with three groups using a pre-test and post-test control group quasi-experimental design. All groups were taught using the FC model. The participants of the study consisted of 98 undergraduate students. The findings revealed that the use of GenAI as a metacognitive scaffolding tool in FC created significant differences in extrinsic motivation, CT, self-efficacy, engagement, problem-solving, metacognitive strategy use, and learning flexibility. However, GenAI did not lead to significant changes in intrinsic motivation, and its impact was not uniformly positive across all variables, highlighting the importance of thoughtful implementation. In the FC implementation supported with metacognitive scaffolding, significant differences were identified in self-efficacy and metacognitive strategy use.

The rapid adoption of generative artificial intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. As a result, assessment reform has become a pressing topic in the GenAI era. This paper presents the findings of a pilot study conducted at British University Vietnam exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from “no AI” to “full AI,” enabling educators to design assessments that focus on areas requiring human input and critical thinking. The pilot study results indicate a significant reduction in academic misconduct cases related to GenAI and enhanced student engagement with GenAI technology. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in higher education, promoting academic integrity while leveraging technology’s potential to enhance learning experiences. Implications for practice or policy: Higher education institutions should adopt flexible frameworks like the AIAS to guide ethical integration of GenAI into assessment practices. Educators should design assessments that leverage GenAI capabilities, while supporting critical thinking and human input. Institutional policies related to GenAI should be developed in consultation with stakeholders and regularly updated to keep pace with technological advancements. Policymakers should prioritise research funding into the impacts of GenAI on higher education to inform evidence-based practices.

The integration of generative artificial intelligence (GenAI) into web-based individual formative e-assessments in higher education is a nascent field that warrants further exploration. This study investigated the use of GenAI within an 8-week undergraduate-level research methods course at a university in the United States of America, aiming to understand how students leverage GenAI tools during individual formative e-assessments questions. The research revealed that a significant majority of students initially preferred traditional study resources over GenAI. However, a gradual shift towards more balanced use of both resources was observed, particularly in formative e-assessments involving statistical analysis and calculation questions. In their interactions with GenAI, students primarily used it for multiple-choice and true/false questions, often by directly copying and pasting the question prompt into the GenAI interface. Students were able to discern and accept accurate responses generated by GenAI and reject those that were incorrect or contradicted their existing knowledge. Students’ reported primary motivations for turning to GenAI were to seek answers to assessment items as well as to corroborate the accuracy of their own responses. This study contributes to the growing body of literature empirically investigating actual usage behaviours with GenAI tools and the motivation behind these behaviours. We discuss the implications and limitations of these findings. Implications for practice or policy: Educators should develop AI literacy programmes and integrate them into pedagogy strategies. Educators and researchers need clear guidelines for ethical AI use in formative e-assessments. Educators should encourage students’ critical thinking and source evaluation on the information that GenAI provides.

The rapid advancement of generative Artificial Intelligence (AI) is transforming English Language Teacher Education (ELTE) and prompting renewed debate about the role of emerging technologies in language pedagogy. While current discussions often emphasize either the benefits or the risks of AI, a more balanced perspective is required. This synthetic review examines generative AI as a double-edged technology that offers significant pedagogical opportunities. However, it also raises concerns regarding pedagogical and ethical implications. Drawing on Albert Borgmann’s device paradigm as a conceptual framework, the study critically examines the current literature on AI integration in language education. The review examines the pedagogical opportunities presented by generative AI in the context of language education. Generative AI can support personalized learning, assist teachers in developing interactive instructional materials, provide immediate feedback, and encourage creative collaboration between learners and AI systems. These capabilities may enhance learner engagement and promote more adaptive language instruction. Despite its advantages, generative AI introduces substantial challenges. AI-generated language may lack cultural authenticity and contextual nuance, and dominant training datasets may contribute to linguistic homogenization. Moreover, excessive reliance on AI tools may weaken learners’ critical thinking and creativity. Thus, the study highlights the necessity of a balanced, human-centered framework for incorporating AI into language teaching.

The rapid development of generative artificial intelligence (GenAI) has prompted increasing interest in its pedagogical potential in second language (L2) writing. While prior research has predominantly examined outcomes or learner perceptions, considerably less attention has been paid to the developmental processes through which GenAI mediates critical thinking over time. Conceptualizing GenAI as a human–AI co-regulatory partner, this longitudinal qualitative case study investigates how sustained GenAI-supported interaction shapes critical thinking development in EFL argumentative writing. Drawing on co-regulation theory, metacognitive scaffolding, and cognitive views of critical thinking, the study traces the evolving interactional patterns of four Chinese EFL learners across an eight-week instructional cycle. Data sources include human–AI dialogue transcripts, successive writing drafts, reflective journals, and semi-structured interviews. Integrated longitudinal analysis reveals diverse developmental trajectories influenced by learner beliefs about AI, epistemic orientation, and instructional framing. The findings highlight conditions that enable or constrain co-regulation and offer practical insights for designing AI-mediated writing instruction that fosters critical thinking.

Over the past three decades, the evolution of technology has dramatically reshaped the information landscape, making it easier to access and simultaneously easier to distort. The advent of artificial intelligence (AI), particularly generative tools like ChatGPT and CoPilot, has further complicated the pursuit of information literacy, posing significant challenges for educators, librarians and students alike. This paper explores the implications of integrating generative AI (GenAI) tools into educational and professional settings, emphasising the necessity of critical thinking and the development of robust information literacy skills to discern the credibility and authority of AI-generated content. By examining the Association of College and Research Libraries’ (ACRL) ‘Framework for Information Literacy for Higher Education’, this paper provides strategies to identify risk areas related to AI integration as well as produce use cases for large language model (LLM) GenAI tools, including a flowchart for determining when to make use of GenAI, a toolkit for positive/effective use cases, and a rubric for assessing information literacy and critical thinking. While AI tools can offer valuable educational opportunities, their propensity to generate misleading or inaccurate information necessitates a careful and informed approach to their use. This paper concludes with a call for ongoing vigilance in maintaining academic integrity and underscores the importance of continuously questioning the reliability of AI outputs in educational contexts.

Generative artificial intelligence (AI) is rapidly transforming the way students learn in higher education, particularly with the advent of ChatGPT. University students are increasingly using ChatGPT for information, idea generation, text production, problem-solving, and to seek assistance with assignments. Recent research shows that the use of ChatGPT among university students has significantly increased, with surveys indicating that over 90% of students have utilized generative AI tools for their studies. This research examines the relationship between the use of ChatGPT with critical thinking disposition skills among University students. Critical thinking is a process of analyzing information, evaluating arguments, making decisions and judgments, problem solving, and reflecting on evidence to reach conclusions. The use of ChatGPT can offer students individualized learning, instant feedback, and cognitive assistance, but it remains unclear if it can boost students' critical thinking abilities. Properly and critically leveraging ChatGPT could support students' analytical and logical thinking skills and problem-solving capabilities, as it might encourage them to think about various perspectives and evaluate the generated texts or responses. However, other studies caution that relying too much on ChatGPT might decrease independent thinking, intellectual effort, and active engagement with material. AI research studies have revealed that over-reliance on AI-generated responses can result in superficial learning, reduced originality, memory problems, and diminished critical thinking abilities. Educational and cognitive science research indicate that there may be a negative impact on the analytical thinking of students who directly use AI tools without questioning or evaluating the content generated by the AI.Educational and cognitive science research also suggests that students who heavily rely on AI tools and tools may have a lower level of analytical thinking than students who question or evaluate the content produced by the AI tool. The study employed a quantitative correlational research design to examine the frequency of the students' use of ChatGPT and the relationship between the frequency of using ChatGPT and their critical thinking disposition abilities at the university level. The data are collected with a semi structured questionnaire that includes items for students' frequency of academic use of ChatGPT and critical thinking skills including analysis, inference, evaluation, interpretation and self-regulation. The strength and direction of the relationship between these factors were analyzed using statistical methods. The findings should provide insight into the impact of ChatGPT on the development of advanced thinking skills and the potential pitfalls of relying too heavily on AI in education. This research extends the body of literature on AI learning by highlighting the importance of responsible and conscious use of ChatGPT in higher education. Furthermore, the results could inform the development of educational practices, policy, and leadership that are effective in implementing AI tools while supporting and strengthening student critical thinking skills.

Purpose This paper aims to explore the integration of Generative Artificial Intelligence (GenAI) with established educational theories to enhance business education in diverse classrooms. It demonstrates how GenAI can personalize learning experiences, foster critical thinking and support multicultural classrooms by providing inclusive, culturally relevant content. Design/methodology/approach The study aligns GenAI capabilities with Cognitive Load Theory, Constructivist Learning Theory, Multicultural Education, Sociocultural Theory and Universal Design for Learning. A comprehensive review of literature and theoretical frameworks underpins a novel approach to integrating these theories with GenAI to enhance business education. Findings Integrating GenAI with educational theories can personalize learning, manage cognitive load, foster active and reflective learning, promote cultural inclusivity and support social interaction. Each theory contributes distinct benefits, such as enhancing students’ critical thinking, supporting diverse learning needs and creating dynamic, interactive learning environments. Research limitations/implications While this framework proposes integrating GenAI into business education, it lacks empirical validation. Future research should focus on the long-term effects on student retention and performance, particularly among diverse populations. Comparative studies with traditional teaching methods are needed to assess the benefits and challenges of GenAI-enhanced learning environments. Practical implications Implementing GenAI-driven strategies in business education can create dynamic, responsive learning environments, preparing students for the global marketplace with improved skills in critical thinking and cultural competence. Social implications Incorporating GenAI in business education supports diverse student populations by addressing their specific educational needs and cultural contexts. This approach fosters social equity by providing inclusive learning experiences, helping students from various backgrounds thrive academically and professionally. Originality/value This paper uniquely combines GenAI with multiple educational theories, offering a comprehensive framework for creating culturally responsive and personalized business education. It addresses a gap in the literature by demonstrating how GenAI can align with educational frameworks to support diverse learning environments.

Artificial intelligence (AI) has emerged as a transformative tool, integrated across various sectors. In education, AI has generated significant excitement among students for its potential to enhance learning experiences. However, concerns about overreliance on AI temper this enthusiasm, as it may undermine the development of critical thinking skills. Numerous studies have highlighted the risks associated with students’ excessive use of generative AI (GAI) in academic tasks, noting its potential to diminish cognitive abilities. However, the optimal use of AI to enhance students’ critical thinking skills remains under-researched. Therefore, this study seeks to answer the question: How does generative AI influence students’ critical thinking, self-efficacy, and decision-making? This study aims to explore the synergic relationship between human intelligence and artificial intelligence in augmenting essential thinking skills among students and building upon their existing cognitive resources through self-efficacy, learning motivation, and decision-making. Specifically, it explores the cause-and-effect connections among GAI, self-efficacy, decision-making, learning motivation, and critical thinking skills. A quantitative methodology used an online questionnaire to collect responses from 165 undergraduate, master’s, and doctoral students. Statistical analyses, including bootstrapping techniques, were conducted to examine direct and indirect effects. The results revealed that GAI has a significant positive influence on self-efficacy, learning motivation, decision-making, and critical thinking skills. In turn, self-efficacy, learning motivation, and decision-making significantly impact critical thinking skills. The mediating results indicated that GAI can indirectly boost students’ critical thinking by enhancing self-efficacy, learning motivation, and decision-making. This suggests that AI capabilities can transform the cognitive learning process.

The rapid integration of generative artificial intelligence (GenAI) into higher education has sparked debates about the future role of teachers (Chan & Tsi, 2024), including in providing feedback information to students. While GenAI offers unprecedented accessibility and immediacy, this presentation argues that teachers' expertise remains irreplaceable in productive feedback – i.e., processes in which students make sense of information about their performance and use it to improve the quality of their work or learning strategies (Henderson et al., 2019, p. 1402). Drawing on a large-scale, cross-institutional survey involving 6,960 Australian university students (Henderson et al., 2025), this Pecha Kucha highlights students' perceptions of GenAI versus teacher feedback. The quantitative analysis revealed that nearly half of them (49.7%) reported using GenAI for feedback. However, they rated teacher feedback as more helpful and significantly more trustworthy. While 83.9% found GenAI feedback helpful, only 60.1% considered it trustworthy, compared to 90.5% who trusted teacher feedback. This trust gap may reflect the inconsistent quality identified in GenAI's feedback comments (Venter et al., 2024). The thematic analysis of 5,736 open-ended responses from students who used GenAI for feedback yielded 8,498 coded instances, revealing four interrelated characteristics in which teacher feedback was perceived as outperforming GenAI. Contextualisation and Relevance: Teacher feedback was perceived as more sensitive to specific assignment contexts (95.2% of 669 instances rated GenAI as less contextualised than teacher feedback) and more relevant to learning objectives (84.6% of 123 instances rated GenAI as less relevant). This contextual awareness enables teachers to identify what matters within disciplinary and course-specific frameworks. Reliability and Accuracy: Students perceived teacher feedback as significantly more reliable and trustworthy (95.4% of 1143 instances), reflecting teachers' ability to provide more trustworthy and accurate guidance without the hallucinations and factual inaccuracies that can appear on GenAI outputs. Relational Significance: Teachers offered more personal, connected feedback experiences (93.8% of 471 instances), providing the interpersonal recognition essential for productive learning relationships. This relational dimension cannot be replicated by GenAI’s algorithmic responses. Expertise: Students recognised teachers as more authoritative sources (88.2% of 119 instances), valuing their disciplinary knowledge and pedagogical understanding of student development trajectories. Students' evaluation of feedback is fundamentally shaped by perceptions of source credibility (Bearman et al., 2024), which may explain why students perceive teacher feedback as more trustworthy than GenAI's. Research demonstrates this selective engagement: uptake of content-focused GenAI feedback was considerably lower than form-focused feedback(Ziqi et al., 2024), suggesting students recognise GenAI's limitations for substantive guidance requiring disciplinary expertise. This translates into learning outcomes, with students not only perceiving instructor feedback as more useful but also demonstrating significantly greater lab score improvements than those receiving GenAI feedback (Er et al., 2025). GenAI may create opportunities for educators to focus on what they do best: providing expert, contextualised, and relationally-grounded feedback within authentic learning relationships. This potentially positions teacher expertise as increasingly valuable, with educators prioritising higher-level pedagogical responsibilities, such as developmental guidance, facilitating critical thinking, and disciplinary enculturation, while GenAI supports lower-level feedback processes, like grammar correction and initial draft review. Students appear to already recognise this distinction, trusting teachers for more substantive, transformative feedback while appreciating GenAI's supplementary role for immediate, accessible guidance.