Understanding the Role of Learning Analytics and Generative Artificial Intelligence on Decision-Making and Learning Design Practice in Higher Education

Instructors use learning analytics and generative AI differently in learning design. Claassen et al. (2026), a quantitative-ethnography study of 11 focus groups with instructors at a large Australian university using Epistemic Network Analysis (ENA) and Self-Determination Theory (SDT) as an interpretive lens, find that LA-informed design discussions centre on context, course-level design, and creative problem-solving, while GenAI discussions centre on assessment design and designing for student self-determination. Across both, instructors consistently combine technological affordances with contextual considerations and creative problem-solving. Supporting instructors' basic psychological needs — autonomy, competence, relatedness — fosters creative, effective learning design.

Key Findings

• LA and GenAI support different parts of learning design. In LA discussions, the strongest co-occurrences were between contextual information, course-level design, and creative problem-solving (the technology-role↔context connection was strongest, 0.32). In GenAI discussions, the strongest were between technology role and assessment design (0.26) and technology role and design for student self-determination (0.15).
• Instructors use LA for problem-solving across design granularity. LA (e.g., teaching dashboards) was used in tandem with contextual information to make course-, activity-, and student-support-level adjustments — confirming that LA cannot be meaningfully interpreted without understanding pedagogical intent and context.
• Instructors use GenAI for ideation and assessment development. GenAI was used to create first drafts of assessment elements (case studies, rubrics) and to co-design course elements with students (e.g., co-created assessment rubrics in initial teacher education), guided by their design approach.
• Distinct reasoning patterns. LA discourse emphasized student support in relation to technology more than GenAI did; GenAI discourse elicited stronger emotional responses and centered on assessment design and student self-determination. The technology-role↔student-support connection was notably weaker in GenAI discourse.
• Context and Creativity are central across both. The co-occurrence of technology role, contextual information, and creative problem-solving was high across both LA and GenAI networks — instructors combine tool affordances with context and creativity to solve instructional problems.
• SDT lens: supporting instructor needs matters. Supporting instructors' autonomy, competence, and relatedness fosters creative problem-solving in learning design; collaborative design and shared institutional decision-making support these needs.

Implications

• Use LA and GenAI as complementary, not interchangeable, tools: LA for diagnosing engagement and targeting support; GenAI for ideation and assessment development — a holistic, context-aware integration.
• Ground analytics in pedagogical context. LA interpretation requires understanding learning-design intent, echoing the human-centered learning analytics agenda.
• Support instructors' psychological needs. Institutions should enable collaborative design, shared decision-making, transparent information flow, and autonomy over tool integration to foster creative, need-supportive learning design.
• Co-design assessment with students. Using GenAI to scaffold student participation in rubric/assessment creation supports student autonomy and competence (SDT).

Connected Concepts

• Learning Analytics
• Learning Design
• Self Determination Theory
• Generative AI
• Higher Ed
• Curriculum Design
• AI Ed Evaluation

Connected Articles

• Lodge Adaptive Capabilities GenAI Future 2026 — Adaptive capabilities for a GenAI-integrated future
• Jiang GenAI Activity Theory Disciplines 2026 — Generative AI across disciplines through activity theory
• Instructor Designed AI Tutors Foreign Language Sdt 2026 — Instructor-designed AI tutors and self-determination theory

Citation

Claassen, A., Ebbert, D., Kovanović, V., Mirriahi, N., & Dawson, S. (2026). Understanding the role of learning analytics and generative artificial intelligence on decision-making and learning design practice in higher education. International Journal of Educational Technology in Higher Education, 23, 43. https://doi.org/10.1186/s41239-026-00619-4