51猎奇入口

51猎奇入口Faculty of Education members Dr. Daniel Chang and Dr. Michael Pin-Chuan Lin, along with colleagues at other universities, have been researching an area that fundamentally affects students but is under-explored: how the use of AI tools influences learners鈥� decision-making, strategy adaptation, and achievement.

In 鈥溾�� (2026), Drs. Chang and Lin acknowledge that traditional self-regulated learning (SRL) models have focused on internal regulation within learners through self-reporting and learning analytics. Building on scholarship that integrates postdigital theories with learning analytics, Drs. Chang and Lin argue that learning is inseparable from the technological and ethical contexts in which it takes place. From a postdigital perspective, what has been considered 鈥渋nternal鈥� regulation is actually distributed across environments鈥攔aising questions about agency, the tools used, and how both the learner鈥檚 agency and AI are entangled. These complications, argue Drs. Chang and Lin, merit further examination of how or whether students disclose their use of AI.

Working with a definition of 鈥渁gency鈥� as entangled with 鈥渁lgorithms, platforms, and the people and institutions standing behind them鈥� and 鈥渘egotiated among teachers, students, and others rather than owned by any single party,鈥� the authors suggest that disclosure decisions about AI use are relational. Instead of representing a moral failing, non-disclosure is influenced by the environment鈥攊ncluding assessment culture, policies, detection systems, and peer norms鈥攁nd driven by perceived risks and costs associated with honesty.

The question to ask, argue Drs. Chang and Lin, is 鈥渉ow assessment cultures, disclosure rules, detection systems, and instructor-student relationships create conditions under which disclosure feels either possible or dangerous.鈥� Empirical evidence shows that trust, comfort with instructors, and normative clarity predict disclosure more than morality alone.

How do students feel about disclosing their use of generative AI? In their study 鈥溾�樷�� (2026), Drs. Chang and Lin turn to undergraduate students鈥� self-reported practices for disclosing their use of generative AI in academic settings鈥攑articularly how worries about judgment, stigma, and dependency influence their willingness to be transparent. Reiterating that the foundational SRL framework was developed before the proliferation of large language models, Drs. Chang and Lin propose GenAI disclosure as an ecological, relational, and ethical practice grounded in trust and negotiated responsibility.

To advance their research programs, Drs. Chang and Lin鈥檚 AIEDU Lab () and their colleagues collaborating with them have been awarded a $415,539 five-year SSHRC Insight Grant (Dr. Lin, applicant; Dr. Chang, co-applicant) and an SSHRC Insight Development Grant (Dr. Ho, BCIT applicant; Drs. Chang and Lin, co-applicants) to explore inclusive, adaptive, and emotional learning in digital environments. Their particular focus is on underrepresented groups in computer science who experience high attrition and a limited understanding of computational concepts. The SSHRC project, 鈥�,鈥� will investigate how MetaMentorAI (MMA) 鈥� an AI-enhanced tool that integrates an AI-powered chatbot for real-time feedback and analytics 鈥� can help CS students develop and refine self-regulated learning (SRL) while studying computational concepts. These research findings will also benefit educators and policymakers outside academia by providing practical tools that integrate SRL support into computer science education curricula, reducing attrition and promoting inclusivity.