Advancing the generative AI in education research agenda: Insights from the Asia-Pacific region

In the expansive landscape of education, the integration of Generative Artificial Intelligence (Generative AI) has initiated a transformative wave, reshaping established paradigms of learning, teaching, and assessment (Baidoo-Anu & Ansah, 2023; Qadir, 2023). The papers presented in this special issue collectively provide a nuanced exploration of the evolving role of generative AI in education, offering valuable insights into the intellectual advancements within the Asia-Pacific region. Authored by scholars from East Asia and Southeast Asia, these contributions not only spotlight the unique perspectives emanating from this vibrant region but also invite intellectual exchange with scholars and practitioners worldwide. This emphasis on the Asia-Pacific context positions this special issue as a catalyst for global conversations on the intersection of artificial intelligence and education.

Against the backdrop of the ChatGPT 3.5 release on 30 November 2022, which initially captivated global attention, the papers featured in this special issue move beyond the transient allure of novelty. The broader discourse on generative AI in education has matured, transcending hastily published position papers that repetitively discussed the pros and cons of generative AI in education throughout the year 2023.

As the novelty effect of ChatGPT gradually subsides, the publications in this special issue emerge as a testament to the field’s incremental maturation. This collection of papers signifies a departure from the published position papers towards more methodologically rigorous studies. Prioritizing academic soundness over expediency of publication, these contributions contribute to a deeper understanding of the intricacies surrounding the integration of generative AI in education. Within this evolving landscape, three overarching and interrelated themes have organically emerged, connecting and contextualizing the diverse range of papers within this special issue. These themes collectively underscore the profound shifts occurring in educational practices and pedagogies, driven by the symbiotic relationship between human intellect and artificial intelligence.

Theme 1: Human-AI collaborative learning ecosystems

In an era characterized by the rapid evolution of AI, especially in the realm of education, a significant concern emerges: the continuous need and challenge of redrawing the line between the natural and the artificial, between human and machine (Bourban & Rochel, 2021). The relentless progress in educational technologies, as exemplified by models like ChatGPT, raises profound questions about the essence of human intelligence and sparks a redistribution or even a redefinition of the roles of educators, learners, and technology (Wong et al., 2017). This transformative shift challenges the established norms and prompts us to reconsider the dynamics of AI-empowered learning.

As AI progresses in sophistication, the educational landscape grapples with a fundamental dilemma: Should humans engage in an ongoing race to outsmart AI, or is there a more harmonious path that envisions a symbiotic intellectual partnership (Tkachuk et al., 2021)? Efforts like Oregon State University’s revision of Bloom’s Taxonomy (Forehand, 2005) represent attempts to unravel the ever-evolving boundary between learners and AI. This revision identifies and critically evaluates the “AI capabilities” and “distinctive human skills” corresponding to each level of the taxonomy, providing guidance for educators and learners as they adapt their teaching and learning goals, and strategies in the era of AI in education. This recalibration aims to ensure that learning journeys remain both meaningful and relevant (Moffet, 2023).

The theme delves into the intricate dance between educators, learners, and AI within collaborative learning ecosystems (Keane & Yeow, 2023). By examining the evolving roles of educators and learners alongside the advancing capabilities of AI, the theme offers insights into the complexities and potential synergies that may define the future of education. This exploration serves as a crucial contribution to the ongoing discourse on the profound transformations underway in education due to the integration of AI.

Under this theme, Li, Ji and Zhan’s “Expert or Machine? Comparing the effect of pairing student teacher with in-service teacher and ChatGPT on their Critical thinking, Learning Performance, and Cognitive load in an integrated-STEM course ” explores the dynamics of “Human-Human” and “Human-Machine” collaborative learning approaches, shedding light on the potential and strengths of AI tools like ChatGPT. While AI-supported learning exhibits advantages, the study calls for refined collaborative learning scaffolding to maximize impact.

Adding depth to our understanding of collaborative learning, Lee’s “Staying Ahead with Generative Artificial Intelligence for Learning: Challenges and Opportunities” identifies common themes and applications of generative AI. It examines the challenges and opportunities in enhancing student learning, proposing the three “R” guidelines: re-tuning assessment methods, re-educating educators and students in data and AI literacy, and re-tweaking existing frameworks for research/practice/policy. These guidelines offer a roadmap for educators and students to navigate the dynamic learning environment with emerging technologies.

Reaching a crescendo in this collaborative narrative, “A Study on the Impact of Generative Artificial Intelligence Supported Situational Interactive Teaching on Students’ “Flow” Experience and Learning Effectiveness” by Shi, Li and Zhang investigates the experiential facets of Generative AI-supported Situational Interactive Teaching. It showcases the effectiveness of this approach in improving students’ learning outcomes and flow experiences across cognitive, skill, and affective domains, underlining its broad applicability.

Collectively, the three papers underscore the need for a balanced approach, blending the strengths of both human and AI entities within collaborative learning environments. They advocate for continuous exploration, improvement of AI capabilities, and the establishment of an ecosystem fostering positive learning experiences. The dynamic interplay among educators, learners, and AI forms the foundation for reshaping educational paradigms, ensuring they remain responsive and relevant in an AI-infused era.

Theme 2: Critical analysis and user perceptions of techno-pedagogical affordances and potential challenges

Theme 2 delves into the critical analysis of techno-pedagogical affordances and potential challenges associated with these advancements. Moving beyond a simple examination of what AI tools offer in terms of features, this theme scrutinizes the broader concept of affordances, considering not just the intended functionalities as designed by the tool’s inventors but also exploring the diverse ways in which users interact with, interpret, and sometimes even redefine these tools (Gibson, 1977). The notion of affordances extends beyond the features explicitly provided by the tool, encapsulating the user’s perspective in shaping the utility and impact of these technologies within educational settings (Pea, 1993).

Users might harness the tools in unanticipated ways, or their interpretations and interactions could be influenced by the tools themselves (Wong et al., 2012). This reciprocal relationship between affordances and user perceptions forms a crucial aspect of the discussion, highlighting the intricate interweaving between the intended functionalities and the diverse ways users employ and perceive AI tools in educational settings. Within this intricate relationship between affordances, user perceptions, and actual tool usage (Sundar, 2020), Theme 2 scrutinizes the techno-pedagogical landscape of Generative AI in education.

Under this theme, Wang, Wang and Su’s “Critical Analysis of the Technological Affordances, Challenges and Future Directions of Generative AI in Education” systematically reviews the technological landscape, providing normative guidelines for future development. The study emphasizes the responsible use of AI applications in education, urging educators to embrace emerging trends and guide students towards a balanced reliance on Generative AI.

Expanding the critical discourse, “A bibliometric analysis of Generative AI in education: Current status and development” by Liu, Wang, Liu, Gao, Xu, Chen and Cheng offers an objective overview of the controversies surrounding the application of generative AI through visual analyses. By identifying hotspots, this paper provides insights into the current state and development of generative AI applications in education, guiding future research and practical implementation.

Adding a unique dimension to this theme, So, Jang, Kim and Choi’s “Exploring public perceptions of generative AI and education: Topic modelling of YouTube comments in Korea” analyses sentiments and prevalent topics in public discourse via social media. The public’s inclination to appreciate the intricate nuances of generative AI’s implications informs a broader understanding of public sentiment and perspectives towards AI in education.

The three papers underscore the importance of meaningful and responsible integration of generative AI in education. By blending technological insights, public perceptions, and considerations of challenges, these papers advocate for guidelines that navigate the multifaceted terrain of AI implementation in education. This theme contributes to a nuanced understanding of the societal and pedagogical implications of introducing generative AI into education.

Theme 3: Teacher perspectives and AI adoption

Theme 3 takes a distinct focus on teachers’ perspectives and the factors influencing the adoption of AI in educational practices. While Themes 1 and 2 provide a holistic view, encompassing both learners and educators, Theme 3 hones in on the crucial role of teachers in the educational ecosystem. This targeted focus recognizes teachers as central figures, pivotal in orchestrating the learning experience and facilitating the synergistic relationship between learners and AI (Holstein & Olsen, 2023).

The choice to delve into teachers’ perspectives within Theme 3 is strategic, acknowledging that teachers serve as not only facilitators of knowledge but also as catalysts for fostering critical thinking, creativity, and adaptability – the essential skills for 21st-century lifelong learners (Sulaiman & Ismail, 2020). Teachers, armed with the right perceptions and skills, play a pivotal role in leveraging generative AI tools to not only enhance content mastery but also to transform their students into adept navigators of the evolving digital landscape.

The Technological Pedagogical Content Knowledge (TPACK) framework provides a valuable lens through which to explore the nuanced interactions between teachers, AI, and educational content (Mishra et al., 2023). TPACK underscores the need for teachers to integrate technology, pedagogy, and content knowledge seamlessly (Mishra & Koehler, 2006). In the context of generative AI, this framework becomes particularly relevant as it necessitates an understanding of how technology can enhance pedagogy and content delivery without compromising educational goals. Theme 3 seeks to implicitly unravel the layers of TPACK as it pertains to generative AI, exploring how teachers can effectively merge their content expertise, pedagogical skills, and technological acumen in the dynamic educational landscape.

“The Comparison of General Tips for Mathematical Problem Solving Generated by Generative AI with those Generated by Human Teachers” by Jia, Wang, Zhang and Wang compares AI-generated general tips with human-designed ones, unveiling the potential of AI in designing efficient educational tools. The findings emphasize the need for continued refinement of AI capabilities while acknowledging its role as a valuable reference for teachers to enhance mathematical learning.

Shedding light on the challenges faced by educators, Gao, Wang and Wang’s “Exploring EFL University Teachers’ Beliefs in Integrating ChatGPT and other Large Language Models in Language Education: A Study in China” offers a snapshot of the concerns and beliefs of EFL university teachers. By examining their beliefs in integrating Large Language Models (LLMs), the study identifies concerns such as the neglect of traditional resources and excessive reliance. It stresses the need for effective policies and strategies to address challenges posed by the prevalence of LLMs in language education.

Rooted in the Technology Acceptance Model (TAM), Ma and Lei’s “The Factors Influencing Teacher Education Students’ Willingness to Adopt Artificial Intelligence Technology for Information-Based Teaching” explores multifaceted factors influencing teacher education students’ acceptance of AI technologies within the actual teaching process. This paper highlights the crucial role of AI literacy and emphasizes the need to promote the tangible benefits and superiority of AI in teaching.

These three papers intertwine teacher beliefs, concerns, and factors influencing AI adoption. They advocate for tailored strategies and policies that consider the perspectives of educators. They not only explore the challenges faced by educators but also provide insights into strategies for fostering a positive and effective integration of AI into educational settings. The exploration within Theme 3 contributes to a critical reflection of the pivotal role teachers play in shaping the educational landscape amidst the integration of generative AI, emphasizing the need for a strategic alignment of technology, pedagogy, and content knowledge to empower both educators and learners in the digital era.

Conclusion: Towards a research agenda on navigating the generative AI for education landscape

In consolidating the diverse perspectives offered in this special issue, the three themes together illuminate essential dimensions of integrating Generative AI in education. While these themes do not aim to comprehensively cover all facets of this dynamic field, they serve to underscore key considerations foundational to the transformative potential of Generative AI in educational settings.

Within Theme 1, the focus centres on the intricate human-machine relationship, exploring a dynamic interplay that can either be reciprocal or detrimental, contingent upon how the delineation between them is redrawn. This theme aligns with the concept of an “intellectual partnership”, advocating for a balanced approach that leverages the strengths of both human and AI entities within collaborative learning ecosystems.

In a broader context, in considering the impact on traditional notions of learning, we need to consider the evolving landscape of human cognition, in the light of advancements in AI and human-machine collaboration that defy traditional comparisons and extrapolation (Müller, 2017). Understanding the trajectory of these developments, along with the corresponding opportunities and risks, becomes crucial. AI technology has the potential to shape human cognition in profound ways.

It is important to acknowledge that while these transformations in human cognition are taking place, their precise nature, extent and impact remain subjects of ongoing study and exploration. The long-term implications, opportunities, and risks associated with these changes, as well as the progress of AI and human-machine hybrids, are active areas of research and deliberation within our community (Hu et al., 2021; Siemens et al., 2022). It becomes imperative to consider the societal, ethical, and safety implications that emerge as human cognition continues to evolve in tandem with technology. Transitioning to Theme 2, the focus expands to encompass the practical utilization and user perception of AI tools. By acknowledging the intrinsic connection between affordances and user perceptions, Theme 2 advocates for a purposeful and ethical integration of generative AI in education, providing valuable guidelines for navigating the intricate terrain.

A key area of new exploration is the impact of AI tools on student learning and educators’ experiences including their empowerment, competencies and self-efficacies with using such tools (ref. Crompton et al., 2022). By investigating how educators and students interact with and respond to AI-driven educational resources, we gain insights into the potential benefits and challenges that arise. Additionally, by examining the role of educators in guiding and facilitating student engagement with AI tools, we can better understand the dynamics of implementing these technologies in pedagogical contexts.

Theme 2 also considers the broader societal implications of AI integration in education. It delves into the ethical considerations surrounding data privacy, algorithmic bias, and the responsible use of AI tools in educational settings. By addressing these concerns head-on, further research can contribute to the development of frameworks and policies that promote transparency, equity, and student well-being in the rapidly evolving landscape of AI-infused education.

Within Theme 3, the focus is placed on teachers’ perspectives, recognizing their crucial role as facilitators and catalysts in the educational ecosystem. This theme aligns with the Technological Pedagogical Content Knowledge (TPACK) framework, emphasizing the importance of teachers seamlessly integrating technology, pedagogy, and content knowledge.

By delving into teachers’ TPACK within the context of AI, we can uncover gaps in their understanding, skills, and attitudes towards AI. This exploration informs the development of targeted professional development initiatives (Seufert et al., 2021), as there is currently limited research on teachers’ TPACK specifically related to discerning the reasons behind challenges in teaching AI within the educational domain. Through an examination of the multifaceted factors that influence teachers’ adoption of AI technologies, a research agenda for Theme 3 offers valuable insights that can contribute to fostering a positive and effective integration of AI in educational settings.

The discourse on generative AI signifies a significant shift in perspective. It moves beyond perceiving AI as a substitute for human intelligence and instead positions it as a tool for amplifying intelligence. Rather than replacing the intelligence of educators and learners, generative AI has the potential to enhance their abilities and provide advanced support in the orchestration of learning. This conceptual transformation aligns with the concept of “augmented intelligence” (Dave & Mandvikar, 2023), where generative AI becomes a complementary force that empowers educators and learners to reach new levels of creativity, critical thinking, and adaptability within the ever-evolving educational landscape.

The amalgamation of these themes calls for a holistic, nuanced approach that acknowledges the symbiotic relationship between human intellect and artificial intelligence. The discourse presented in this special issue serves as a catalyst for ongoing conversations, fostering a deeper understanding of the transformative potential of Generative AI in shaping the future of education.

As a final but important note, our advocacy for the thoughtful integration of new AI-powered techno-pedagogical models is firmly rooted in established learning theories, emphasizing the need to avoid adopting AI for the sake of following trends. Recent research on LLMs highlights notable advancements in Generative AI, particularly in tailoring Language Models for educational purposes. It is paramount to approach these innovations with a discerning and pedagogically sound perspective.

The dedicated efforts of computer scientists and researchers have resulted in the refinement of Language Models that align with the unique needs and requirements of diverse educational contexts. Through the utilization of sophisticated algorithms, application of machine learning techniques, and leveraging domain-specific datasets, customized Language Models are being meticulously designed to enhance educational experiences. These models aim to facilitate personalized learning and provide targeted support to educators and learners.

Crucially, our advocacy extends to the ongoing collaboration between computer scientists and educators, which holds immense promise for continuously improving and adapting LLMs. This collaboration ensures that the development of AI tools for education is not only cutting-edge but, more importantly, effective and aligned with the principles of sound educational theory. As such, it is imperative for stakeholders in education to approach AI integration with a discerning eye, explicitly emphasizing the importance of grounding these advancements in pedagogical considerations. By doing so, we can maximize the transformative potential of AI in educational settings, fostering an environment where technological innovation aligns seamlessly with established learning theories for optimal educational outcomes.