MATLAB AI Tutor Course Policy
Purpose
Interview an instructor to create a course-specific AI-POLICY.md file. The
file should be suitable to upload to a learning management system, share with
learners, and install locally for MATLAB AI tutoring sessions so assignment
guardrails can enforce the instructor's rules.
Use this skill before a course pilot, when adopting the tutor for graded work,
or when an instructor wants one policy that applies consistently across
homework, labs, projects, quizzes, exams, and instructor-facing materials.
Interactive Interview
Run the interview in short rounds. Ask at most three questions at a time and
summarize choices before generating the policy. If the instructor supplies a
syllabus, assignment description, or existing policy, extract answers from it
first and ask only about gaps.
Required policy requirements:
- Course title, term, instructor, and contact or support path.
- Course-wide AI-use stance: encouraged, allowed with limits, restricted, or
prohibited except when explicitly authorized.
- Rules by activity type: homework, labs, projects, quizzes, exams, take-home
assessments, and instructor-facing content.
- Allowed AI tutor help: concept explanations, analogous examples, debugging,
code review, tests, reflection, transcript logs, and session reports.
- Restricted AI tutor help: final solutions, full programs, answer keys,
hidden test bypassing, unauthorized collaboration, and polishing work before
a meaningful learner attempt.
- Attribution requirements: whether learners must disclose tutor use, include
prompts/transcripts, cite AI assistance, or submit session reports.
- Data and privacy boundaries: what learners should avoid sharing.
- Local enforcement level for MATLAB AI Tutor guardrails.
- Effective date and review cadence.
Read references/policy-interview.md for the
interview sequence, enforcement levels, and policy decision matrix.
Read references/ai-policy-template.md before
writing AI-POLICY.md.
Read references/policy-examples.md when the
instructor asks for examples, wants help choosing policy strictness, or needs
calibrated wording for homework, labs, projects, quizzes, exams, or
instructor-facing solution generation.
Output Workflow
- Interview the instructor until required policy requirements are known.
- Summarize the interpreted policy choices and ask for confirmation when
anything is ambiguous or high stakes.
- Generate
AI-POLICY.md in the current working directory unless the user
specifies another writable course folder.
- Use learner-facing language: clear, direct, and suitable for an LMS.
- Include a "Local MATLAB AI Tutor Enforcement" section that assignment
guardrails can read.
- Include a "Policy Summary for Tutor Guardrails" block with compact rules for
tutoring sessions.
- Tell the user where the file was written and how to use it with the tutor.
Local Installation Rules
- The policy filename must be
AI-POLICY.md.
- The preferred local install location is the course or tutoring session working
directory.
- When a tutoring session starts,
matlab-apply-assignment-guardrails should
look for AI-POLICY.md in the current working directory and apply it before
general guardrail defaults.
- If multiple policies are present, use the nearest policy in the current
course/session directory and state which file is active.
- If no policy is present, use conservative default guardrails and ask whether
the task is graded or policy-constrained when unclear.
Output Constraints
- Do not invent institutional policy, honor-code language, or legal claims.
- If the instructor is unsure, mark the policy item as "Instructor default:
conservative" and write a clear placeholder for later revision.
- Keep the policy actionable for learners and enforceable by the tutor.
- Do not create separate README files. The policy artifact is
AI-POLICY.md.
Examples
This demo includes an example learner-facing policy at
assets/examples/ai-policy-intro-matlab-coached.md, relative to the demo folder
that contains skills/ (not relative to this skill folder). Use it as a
structural example only; replace the course name, activity rules, disclosure
requirements, and local enforcement settings with the instructor's confirmed
policy choices.
1---2name: matlab-create-ai-policy3description: Use when an instructor wants to create, interview for, configure, install, update, or review a course AI-use policy for MATLAB AI tutoring. Produces an AI-POLICY.md file for LMS sharing and local tutoring-session enforcement by assignment guardrails.4license: MathWorks BSD-3-Clause (see LICENSE)5---6
7# MATLAB AI Tutor Course Policy
8
9## Purpose
10
11Interview an instructor to create a course-specific `AI-POLICY.md` file. The
12file should be suitable to upload to a learning management system, share with
13learners, and install locally for MATLAB AI tutoring sessions so assignment
14guardrails can enforce the instructor's rules.
15
16Use this skill before a course pilot, when adopting the tutor for graded work,
17or when an instructor wants one policy that applies consistently across
18homework, labs, projects, quizzes, exams, and instructor-facing materials.
19
20## Interactive Interview
21
22Run the interview in short rounds. Ask at most three questions at a time and
23summarize choices before generating the policy. If the instructor supplies a
24syllabus, assignment description, or existing policy, extract answers from it
25first and ask only about gaps.
26
27Required policy requirements:
28
291. Course title, term, instructor, and contact or support path.
302. Course-wide AI-use stance: encouraged, allowed with limits, restricted, or
31 prohibited except when explicitly authorized.
323. Rules by activity type: homework, labs, projects, quizzes, exams, take-home
33 assessments, and instructor-facing content.
344. Allowed AI tutor help: concept explanations, analogous examples, debugging,
35 code review, tests, reflection, transcript logs, and session reports.
365. Restricted AI tutor help: final solutions, full programs, answer keys,
37 hidden test bypassing, unauthorized collaboration, and polishing work before
38 a meaningful learner attempt.
396. Attribution requirements: whether learners must disclose tutor use, include
40 prompts/transcripts, cite AI assistance, or submit session reports.
417. Data and privacy boundaries: what learners should avoid sharing.
428. Local enforcement level for MATLAB AI Tutor guardrails.
439. Effective date and review cadence.
44
45Read [references/policy-interview.md](references/policy-interview.md) for the
46interview sequence, enforcement levels, and policy decision matrix.
47
48Read [references/ai-policy-template.md](references/ai-policy-template.md) before
49writing `AI-POLICY.md`.
50
51Read [references/policy-examples.md](references/policy-examples.md) when the
52instructor asks for examples, wants help choosing policy strictness, or needs
53calibrated wording for homework, labs, projects, quizzes, exams, or
54instructor-facing solution generation.
55
56## Output Workflow
57
581. Interview the instructor until required policy requirements are known.
592. Summarize the interpreted policy choices and ask for confirmation when
60 anything is ambiguous or high stakes.
613. Generate `AI-POLICY.md` in the current working directory unless the user
62 specifies another writable course folder.
634. Use learner-facing language: clear, direct, and suitable for an LMS.
645. Include a "Local MATLAB AI Tutor Enforcement" section that assignment
65 guardrails can read.
666. Include a "Policy Summary for Tutor Guardrails" block with compact rules for
67 tutoring sessions.
687. Tell the user where the file was written and how to use it with the tutor.
69
70## Local Installation Rules
71
72- The policy filename must be `AI-POLICY.md`.
73- The preferred local install location is the course or tutoring session working
74 directory.
75- When a tutoring session starts, `matlab-apply-assignment-guardrails` should
76 look for `AI-POLICY.md` in the current working directory and apply it before
77 general guardrail defaults.
78- If multiple policies are present, use the nearest policy in the current
79 course/session directory and state which file is active.
80- If no policy is present, use conservative default guardrails and ask whether
81 the task is graded or policy-constrained when unclear.
82
83## Output Constraints
84
85- Do not invent institutional policy, honor-code language, or legal claims.
86- If the instructor is unsure, mark the policy item as "Instructor default:
87 conservative" and write a clear placeholder for later revision.
88- Keep the policy actionable for learners and enforceable by the tutor.
89- Do not create separate README files. The policy artifact is `AI-POLICY.md`.
90
91## Examples
92
93This demo includes an example learner-facing policy at
94`assets/examples/ai-policy-intro-matlab-coached.md`, relative to the demo folder
95that contains `skills/` (not relative to this skill folder). Use it as a
96structural example only; replace the course name, activity rules, disclosure
97requirements, and local enforcement settings with the instructor's confirmed
98policy choices.