Using social learning theories to explore the role of generative Artificial Intelligence (AI) in collaborative learning
Open accessAbstract
This opinion piece highlights the integral role of generative Artificial Intelligence (AI) in learning within Higher Education Institutions (HEIs). Employing social learning theories, this opinion piece aims to explore generative AI as a stakeholder in learning. By weaving in social constructivist and learning theories, this opinion paper aims to uncover the capacity of generative AI to facilitate and enhance the learning process. Central to this opinion piece proposition is cultivating a learning community that leverages AI's potential as a new learning stakeholder. This opinion piece aims to contribute to ongoing discussions in the field of learning development by offering a fresh outlook on how AI can be an asset in knowledge co-creation and collaborative learning. The paper does this in the following ways: (1) highlights how generative AI can effectively contribute to learning and knowledge co-creation, and (2) provides some guidance for integrating generative AI in collaborative learning.
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Equipping students with AI skills is necessary for their employability and for functioning as AI-literate global citizens in an AI-enabled economy.
“The urgency for equipping students with AI skills is not just a matter of professional development but a necessity for their employability and AI-literate functioning global citizens in an AI-enabled economy.”
From Developing a conceptual framework for Artificial Intelligence (AI) literacy in higher education (2024) · cites this paper as Zhou and Schofield, 2024
The Conclusion calls for training in the use of generative AI, and “Integrate generative AI to enhance collaboration and inclusivity” identifies communication, problem-solving, and leadership as employability skills. Neither section says AI skills are necessary for employability or for functioning as AI-literate global citizens in an AI-enabled economy. (LLM, conf=0.84)
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Cites
ChatGPT for good? On opportunities and challenges of large language models for education
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GPT detectors are biased against non-native English writers
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023) · Patterns · doi:10.1016/j.patter.2023.100779 · Cited by 673 · cited 1× · see citing works
“However, there is limited empirical evidence to support the effectiveness and efficiency of AI detectors, with authors such as Liang et al. (2023) raising concerns and research gaps about the fairness and robustness of the AI detector tools, especially against non-native English writers.”
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Social Constructivism: A New Paradigm in Teaching and Learning Environment
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Sorted by citations
Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295 · see citing works
“A social constructivism framework emphasises learning as a collaborative, interactive process where knowledge is constructed within a social context, for example, interactive workshops or group activities [25].”
Zhou, X., Teng, D., & Al-Samarraie, H. (2024) · Education Sciences · doi:10.3390/educsci14121302 · Cited by 119 · see citing works
“Zhou and Lilian [25] posited that GenAI has the potential to function as a new stake-holder in the students’ learning journal, assisting students in independent learning by offering constructive insights, feedback, and guidance to students, a 24/7 study buddy and teaching assistant.”
Self-citationShen, M., Shen, Y., Liu, F., & Jin, J. (2025) · BMC Nursing · doi:10.1186/s12912-025-03115-8 · Cited by 76 · see citing works
Developing a conceptual framework for Artificial Intelligence (AI) literacy in higher education
Zhou, X., & Schofield, L. (2024) · Journal of Learning Development in Higher Education · doi:10.47408/jldhe.vi31.1354 · Cited by 32 · see citing works
Self-citation
- Authors
- Zhou, X., & Schofield, L.
- Affiliations
- Queen Mary University of London (GB)
- Venue
- Journal of Learning Development in Higher Education
- Published
- 2024-03-27
- Issue
- 30
- ISSN
- 1759-667X (ISSN-L)
- OpenAlex
- W4393226825
- Cited by
- 45
- References
- 5
- Citations by year
- 2026: 12 · 2025: 24 · 2024: 9
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Learning Development in Higher Education (publishedVersion, cc-by)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, cooperative learning, social learning theory, social constructivism, knowledge co-creation, learning communities, higher education, AI in teaching
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