Date
Publisher
arXiv
As generative AI permeates computing instruction, the emergent design challenge is to configure each tool's pedagogical role, authority, and accountability for the instructional work it performs. We argue for instructional governance by design: governance should be encoded in a teaching tool's interaction model, constraints, and workflow. We introduce a multidimensional framework that characterizes AI teaching tools through (1) pedagogical grounding, (2) AI instructional authority, (3) human accountability and control, (4) learner agency and cognitive engagement, (5) context specificity and boundary setting, and (6) evaluation visibility and revision. These dimensions yield governance profiles that help educators align tools with specific purposes and educational stakes. We develop the position through a comparative analysis of a portfolio of AI teaching tools across computing and first-year engineering: rubric-anchored GTA simulations, reflection-oriented code companions, staff-reviewed forum-response systems, TA-supervised diagram generators, and course-specific code-style coaches. These cases show how common instructional functions call for different combinations of rubrics, learning-theory commitments, approval gates, supervision, and course-specific constraints. We further apply the framework to selected published tools to demonstrate its use beyond a single institutional portfolio. From these cases, we identify reusable design questions for aligning governance with instructional stakes, human capacity, and intended learning processes. This position reframes responsible AI integration as a curricular and interaction-design challenge and offers a common vocabulary for tool builders, instructors, and researchers to design, compare, and evaluate AI-mediated learning environments.
Study at a glance
- Question: How can AI teaching tools be designed to properly manage their instructional authority and human oversight?
- Setting and data: A portfolio of AI teaching tools in computing and first-year engineering, and selected published tools.
- Method: Comparative analysis of AI teaching tools (comparative analysis).
Why it matters
As generative AI permeates computing instruction, an emergent design challenge is configuring each tool's pedagogical role, authority, and accountability for the instructional work it performs.
Key findings
- In college computing and engineering, AI instructional governance is shaped through tools' interaction models, constraints, and workflows to align with educational stakes.
- Common instructional functions call for different combinations of rubrics, learning-theory commitments, human approval gates, supervision, and course-specific constraints.
- Responsible educational AI integration centers on an interaction-design challenge that balances machine authority, human accountability, and student cognitive engagement.
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