Instructional Governance by Design: A Framework for AI in Computing Education

arXiv:2609.26098v1 Announce Type: new Abstract: 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 learn

Framework for AI in education proposed

The framework aims to responsibly integrate AI in education, potentially improving learning through better instructional design, but it also raises governance challenges about authority and accountability. The effects depend on how well these frameworks are implemented.

This paper proposes a framework for integrating AI tools into computing education, focusing on governance by design. It aims to provide a structured approach for educators and tool builders to align AI with pedagogical goals. The implications are primarily neutral as it offers a conceptual tool rather than an outcome.

The paper presents a framework for governing AI in education, emphasizing alignment with educational goals and human oversight. It offers a structured approach to integrating AI responsibly, which is materially optimistic for human flourishing.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   6489e80b-e717-4c1c-8dd6-273cadf28875
           https://arxiv.org/abs/2609.26098
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -17  (Mixed or uncertain)
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CONFIDENCE 75%