Establish the workload implications of responsible AI integration for faculty

Establish a stable cross-institution estimate of the workload required for responsible generative-AI integration in software-engineering education, including evaluating process evidence, administering oral assessments, curating AI-generated feedback, maintaining tool policies, and supporting privacy and accessibility.

Background

The paper explains that responsible AI integration changes faculty work substantially. Instructors may need to redesign assignments, determine which AI capabilities are compatible with particular learning objectives, inspect process evidence, conduct oral or live checks, curate AI-generated feedback, protect student and client data, and keep policies current as tools evolve.

Although faculty studies report pedagogical costs associated with policy gaps, the paper explicitly notes that the literature does not yet provide a stable cross-institution estimate of this workload. Establishing such an estimate would help institutions assess the feasibility and resource requirements of judgment-centred AI integration rather than treating the burden as an unquantified implementation issue.

References

The literature does not yet provide a stable cross-institution estimate of this workload, so the issue should be treated as an implementation constraint rather than a quantified universal burden.

— Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning  (2609.29473 - Mahmoud, 24 Sep 2026) in Section 4.2, “Faculty perspective: assessment validity, capability, workload, and policy coherence”