The response of pre-service teachers to a compulsory research project

This paper draws on 159 survey responses of fourth year Education students as they embark on a research project based in their internship school. The project explores predisposition to research and the level of preparedness to undertake a research project. The students who met most frequently with their supervisor and showed higher research self-efficacy were also most likely to want to undertake postgraduate study and reacted positively to good personal support and a feeling of belonging to a research “community”. Many felt they were “unconnected” to a learning community, and specifically to their supervisor. They were generally positive about their ability to do the tasks, such as finding and writing up literature, even though they were concerned about managing the overall commitment. Despite this they were optimistic about completion. Only 5% were sure they would undertake postgraduate study in the future, and 65% were sure they would not do so. In an environment where there is commitment to promoting research skills consistent with the needs of a knowledge society and drawing on evidence to inform practice, the findings not only raise the question of how best to support and give meaning to early research endeavours of preservice teachers, but also highlights the challenge of achieving this against a high level of disinterest in further tertiary study.     

Human and artificial intelligence collaboration for socially shared regulation in learning

Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human-AI collaboration by introducing a novel trigger concept and a hybrid human-AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human-AI collaboration in facilitating effective SSRL in teaching and learning.

Practitioner notes

What is already known about this topic

• For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor.
• Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning.
• It is questionable whether traditional theories of how people learn are useful in the age of AI.

What this paper adds

• Introduces a trigger concept and a hybrid Human-AI Shared Regulation in Learning (HASRL) model to offer insights into how the human-AI collaboration could occur to operationalize SSRL research.
• Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning.
• Provides clear suggestions for future human-AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills.

Implications for practice and/or policy

• Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning.
• Researchers and practitioners could benefit from methodological development incorporating human-AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.