Aims of the Special Issue
This special issue aims to advance studies and practices in integrating generative AI in teaching and learning by harnessing the lens of constructivist-oriented active learning principles (Adams, 2006). With a focus on understanding how educators, learning developers and researchers integrate generative AI into teaching and learning (Zhou & Schofield, 2024), we aim to understand how generative AI tools like ChatGPT can scaffold prior knowledge and experiences to create new knowledge. Articles in the issue will explore how these technologies enable a knowledge-creation process that supports constructivist learning approaches, encouraging active knowledge generation while tackling the challenges in teaching and learning with generative AI.
Topicality
Several existing pieces of literature support the need for this special issue. Rasul et al. (2023) presented possibilities for AI tools, such as ChatGPT’s role in facilitating constructivist learning. Zhou and Schofield (2024) proposed suggestions for using social learning theories to explore the role of generative Artificial Intelligence (AI) in collaborative learning. Essien et al. (2024) discuss the influence of AI text generators on critical thinking skills in UK business schools. Lodge et al. (2023) propose the importance of exploring the potential of AI in enhancing learning experiences. The rapid development and widespread adoption of generative AI tools like ChatGPT in recent years have created a pressing need for research on their educational implications. The literature published on AI in teaching and learning evidences this.
Policy and societal trends are also pushing for AI-literate graduates. For example, the Russell Group guidelines on AI highlight the essential need for AI literacy (Russell Group, 2023). The Alan Turing Institute also champions this push by developing AI guidance to empower businesses and individuals to embrace AI (The Alan Turing Institute). Organisations such as (Okemwa, 2024), Deloitte (Deloitte, 2023), World Economic Forum (World Economic Forum, 2024) and McKinsey (McKinsey, 2023) emphasise the need for organisations to invest in AI literacy programs.
Indicative list of themes and key features of the Special Issue
The guest editors invite abstracts outlining research exploring various teaching and learning approaches that incorporate GenAI in higher education, such as technology-supported constructivism learning (Rasul et al., 2023), active learning processes in incorporating AI (Zhou & Schofield, 2024), and Using social learning theories to explore the role of generative Artificial Intelligence (AI) in collaborative learning and teaching and learning (Ng et al., 2021; Zhou & Schofield, 2024), The importance of exploring the potential of AI in enhancing learning experiences (Lodge et al., 2023), The influence of AI text generators on critical thinking skills in UK business schools (Essien et al., 2024), innovative pedagogical approaches with AI (Paniagua & Braman, 2023), on opportunities and challenges of large language models for education (Kasneci et al., 2023), Cross-disciplinary perspectives (Sáez-Velasco et al., 2024) and ethical implications of AI integration in higher education (Cotton et al, 2023; Perkins et al., 2024).
Themes and research questions
The issue will link to and build on the work of AI in teaching and learning (Lodge et al., 2023; Rasul et al., 2023; Zhou & Schofield, 2024;). Areas of contribution could include, but are not limited to, the following themes:
- How GenAI tools like ChatGPT can facilitate constructivist learning by scaffolding prior knowledge and experiences to create new knowledge. How can educators use generative AI tools to support the process of knowledge creation? How can these tools enhance the learning experience?
- Exploring learning theories, particularly constructivist learning orientations, by examining how learners construct knowledge using generative GenAI. How can learners use generative AI as a ‘critical friend’ or ‘learning support buddy’ to construct knowledge?
- Exploring how generative AI can shape the future of the learning process. Reflections on developing pedagogies that integrate skills, including AI skills in problem-solving and creative thinking, can shape students learning.
- Case studies and evidence-based successful implementation of AI in teaching and learning. Interdisciplinary research in integrating GenAI will be encouraged.
- What ethical considerations should educators consider when integrating AI into teaching and learning?
- Challenges and opportunities in integrating AI in teaching and learning in higher education. We invite contributors to explore questions about the challenges and opportunities of integrating GenAI into constructivist and active learning.
- The role of institutions in enabling AI in active learning, experiential learning and constructivist learning. What is the impact of higher education institutions integrating AI in teaching and learning?
Call for Abstracts
Abstracts are invited on the theme of Integrating Constructivist Approaches and Active Learning in Knowledge Creation with Generative AI.
Referenced abstracts of 250-350 words should include an indicative submission title, study aims, and research question/s.
Key dates:
Abstract submissions: 1 November to 1 December 2024
Acceptance notifications: 30 December 2024
Full paper submissions: 1 February to 1 June 2025
Contact:
Abstract and enquiries to Dr Lilian Schofield
E-mail: [email protected]
Guest Editors:
Dr Lilian Schofield, Queen Mary University of London, UK
Dr Xue Zhou, Queen Mary University of London, UK
Dr Mike Perkins, British University of Vietnam, Vietnam
Dr Lei Fang, Queen Mary University of London, UK
Prof Wang Kai, Anhui Agricultural University, China
Dr Kumaran Rajaram, Nanyang Technological University, Singapore
References
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Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
Deloitte (2023). Generative AI and the future of work the potential? Boundless. https://www2.deloitte.com/content/dam/Deloitte/de/Documents/human-capital/Deloitte_GenAI-Future-of-Work.pdf. Accessed 09/05/2024.
Essien, A., Bukoye, O. T., O’Dea, X., & Kremantzis, M. (2024). The influence of AI text generators on critical thinking skills in UK business schools. Studies in Higher Education, 49(5), 865–882. https://doi.org/10.1080/03075079.2024.2316881
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Paniagua, F., & Braman, J. (2023). Enhancing Programming Education Through Innovative Teaching and Embracing AI. In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) (pp. 2748-2750). IEEE. Doi: 10.1109/CSCE60160.2023.00451.
Perkins, M., Furze, L., Roe, J., MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/q3azde36
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Sáez-Velasco, S., Alaguero-Rodríguez, M., Delgado-Benito, V. and Rodríguez-Cano, S. (2024). Analysing the Impact of Generative AI in Arts Education: A Cross-Disciplinary Perspective of Educators and Students in Higher Education. In Informatics (Vol. 11, No. 2, p. 37). MDPI. https://doi.org/10.3390/informatics11020037
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Zhou, X. and Schofield, L. (2024). Using social learning theories to explore the role of generative Artificial Intelligence (AI) in collaborative learning. Journal of Learning Development in Higher Education. DOI: https://doi.org/10.47408/jldhe.vi30.1031