Evaluating Heutagogy-Based Learning Contracts for Self-Determined Learning in Postgraduate AI Education
DOI:
https://doi.org/10.24036/jtip.v20i1.1163Keywords:
Learner Agency, Artifact-based assessment, AI-supported pedagogy, Educational information systems, CLO-based sprintAbstract
This study evaluated a heutagogy-informed, AI-assisted Learning Contract in a postgraduate Artificial Intelligence course without changing the existing syllabus or Course Learning Outcomes (CLOs). A naturalistic qualitative document analysis examined 28 final Learning Contracts, course records, learning artifacts, and grades using consistent descriptive coding across five CLO-oriented learning segments over one semester. Twenty-seven contracts covered all five CLOs, and all 28 documented resource exploration; however, only three explicitly defined individual learning paths. AI primarily served as a pedagogical assistant (13 contracts), while reflective-ethical support appeared in five. Applied topics showed greater documented depth than ethics and sustainability. The mean final grade was 79.40, interpreted descriptively rather than as evidence of causal impact. Findings indicate that the Learning Contract linked academic requirements, student planning, and learning evidence, supporting resource autonomy and evaluation accountability more consistently than explicit pathway autonomy or AI-use reflection. The approach may inform future CLO-based learning analytics and responsible AI documentation.
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