Preprint
Machine Learning

The promise and challenges of generative AI in education

Michail N. Giannakos(University of Agder), Roger Azevedo(University of Central Florida), Peter Brusilovsky(University of Pittsburgh), Mutlu Cukurova(University College London), Yannis Dimitriadis(Universidad de Valladolid), Davinia Hernández‐Leo(Universitat Pompeu Fabra), Sanna Järvelä(University of Oulu), Manolis Mavrikis(University College London), Bart Rienties(The Open University)
September 2, 2024Behaviour and Information Technology392 citations
PDFDOI

392

Citations

24

Influential Citations

Behaviour and Information Technology

Venue

2024

Year

Abstract

Generative artificial intelligence (GenAI) tools, such as large language models (LLMs), generate natural language and other types of content to perform a wide range of tasks. This represents a significant technological advancement that poses opportunities and challenges to educational research and practice. This commentary brings together contributions from nine experts working in the intersection of learning and technology and presents critical reflections on the opportunities, challenges, and implications related to GenAI technologies in the context of education. In the commentary, it is acknowledged that GenAI’s capabilities can enhance some teaching and learning practices, such as learning design, regulation of learning, automated content, feedback, and assessment. Nevertheless, we also highlight its limitations, potential disruptions, ethical consequences, and potential misuses. The identified avenues for further research include the development of new insights into the roles human experts can play, strong and continuous evidence, human-centric design of technology, necessary policy, and support and competence mechanisms. Overall, we concur with the general skeptical optimism about the use of GenAI tools such as LLMs in education. Moreover, we highlight the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices.

Analysis

Why This Paper Matters

This commentary arrives at a critical juncture where generative AI tools like LLMs are rapidly entering educational settings, often without sufficient scrutiny. By convening nine leading experts, the paper provides a much-needed, multi-faceted perspective that balances enthusiasm with caution. Its significance lies in moving beyond hype to articulate concrete opportunities—such as automated feedback and personalized learning design—while rigorously cataloging risks like ethical breaches, disruption of traditional pedagogies, and potential misuse. For practitioners, this paper serves as a foundational reference for developing responsible AI integration strategies.

Technical Contributions

The paper's primary contribution is its structured synthesis of expert opinions, which identifies key areas where GenAI can enhance education:

  • Learning Design: GenAI can assist in creating adaptive curricula and instructional materials.
  • Regulation of Learning: Tools can support self-regulated learning by providing real-time prompts and reflections.
  • Automated Content and Feedback: LLMs enable scalable, personalized feedback and content generation.
  • Assessment: AI can automate formative and summative assessments, though with caveats about validity.

It also systematically outlines limitations and risks:

  • Ethical Consequences: Issues of bias, privacy, and equity are highlighted.
  • Potential Misuses: Over-reliance on AI may degrade critical thinking and teacher-student interactions.
  • Ecosystem-Level Implications: Adoption affects institutional policies, teacher roles, and student agency.

Results

As a commentary, this paper does not report experimental results or quantitative metrics. Instead, its findings are qualitative and consensus-driven: the experts agree on a stance of "skeptical optimism." They emphasize that while GenAI can improve efficiency and personalization, its integration must be guided by continuous evidence, human-centric design, and robust policy frameworks. The paper does not compare models or benchmarks, but its value lies in framing the discourse for future empirical studies.

Significance

This paper has broad implications for the AI and education communities. It provides a roadmap for researchers to investigate the efficacy of GenAI tools in controlled studies, and for policymakers to develop ethical guidelines. By warning against hasty adoption, it encourages a deliberate approach that prioritizes pedagogical soundness over technological novelty. For Neura Market's audience, this underscores the importance of designing AI systems that augment rather than replace human expertise, ensuring that educational technology remains equitable and effective.

generative-ailarge-language-modelseducationlearning-designethicshuman-centric-designpedagogy
Large Language Models

Related Papers