AI-P01 — AI for Universities

Course Code: AI-P01  |  School: School of Artificial Intelligence  |  Cluster: Level 3 — Applied & Sectoral Practice

Level: Intermediate  |  Duration: 5 weeks · 18–22 learning hours  |  Language: English  |  Certificate: Professional Certificate (non-degree)  |  Format: Self-paced with AI support under human supervision

Overview

Universities face a specific version of the AI problem. Their core product is assessed learning, and the assessment instruments most institutions rely on were designed for a world in which producing competent prose was itself evidence of competence. That assumption no longer holds, and no detection technology will restore it.

This course works through the consequences in the order a university actually encounters them: assessment first, because that is where the crisis is; then teaching, where the opportunity is real but easily overstated; then student support and administration, where the gains are largest and least discussed; and finally institutional policy and staff capability.

The course argues for redesign over detection. Detection tools produce false positives at rates that make disciplinary use indefensible, and they disproportionately flag writing by non-native speakers. Learners therefore spend most of their effort rebuilding assessment so that it measures what they intend, and produce a redesigned assessment for a module they actually teach.

Learning outcomes

On completion, a successful learner will be able to:

1. Audit an existing assessment for vulnerability, distinguishing tasks that no longer evidence learning from those that still do.
2. Redesign assessment so that the evidence of learning is process, defence and application rather than produced text alone.
3. Explain why detection tools are unsuitable as a basis for academic misconduct proceedings, and state what may replace them.
4. Design an AI-assisted teaching activity with a defined learning purpose and a verification step for students.
5. Evaluate an administrative or student-support use case for benefit, risk and data protection exposure.
6. Contribute to institutional policy on student and staff use, and to a staff capability programme.

Who this course is for

Lecturers, programme leaders, deans, directors of teaching and learning, quality assurance staff, registrars, and academic developers.

Prerequisites

A current teaching, programme or academic management role. AI-F01 recommended. Learners must bring one module or programme they are responsible for; the assessed work is applied to it.

Syllabus

Module 1 — The assessment problem, stated honestly

Focus. What an essay was evidence of, what it is evidence of now, and why the institution’s first instinct — detect and punish — fails on both technical and equity grounds.

Lessons. 1.1 What assessment was measuring. 1.2 Vulnerability audit of a real assessment. 1.3 Detection tools: accuracy, false positives, and disparate impact. 1.4 Why procedural fairness rules out detection-led discipline.

Core reading. Phillip Dawson, Defending Assessment Security in a Digital World: Preventing E-Cheating and Supporting Academic Integrity in Higher Education (Abingdon: Routledge, 2020), chapters 1–3 and 8.

Deliverable. Vulnerability audit of one assessment you own, with each task classified.

Module 2 — Redesigning assessment

Focus. Constructive alignment applied to the new conditions. Assessing process, defence, application to a local context, and staged submission. Designing tasks where machine assistance is permitted and irrelevant to the difficulty.

Lessons. 2.1 Constructive alignment revisited. 2.2 Process and staged evidence. 2.3 Oral and viva defence at scale. 2.4 Tasks that assistance cannot shortcut.

Core reading. John Biggs & Catherine Tang, Teaching for Quality Learning at University, 4th edition (Maidenhead: Open University Press, 2011), chapters 5–7. David J. Nicol & Debra Macfarlane-Dick, “Formative Assessment and Self-Regulated Learning”, Studies in Higher Education 31, no. 2 (2006): 199–218.

Deliverable. Redesigned assessment with rubric, marking workload estimate and rationale.

Module 3 — Teaching with AI, purposefully

Focus. Activities in which a generative tool serves an identified learning purpose: critique of machine output, comparison of explanations, deliberate error-finding. Distinguishing these from tool demonstrations with no learning objective.

Lessons. 3.1 Naming the learning purpose. 3.2 Critique-based activities. 3.3 Error-finding and calibration exercises. 3.4 Equity of access and the cost objection.

Core reading. José Antonio Bowen & C. Edward Watson, Teaching with AI: A Practical Guide to a New Era of Human Learning (Baltimore: Johns Hopkins University Press, 2024), chapters 3–6. UNESCO, Guidance for Generative AI in Education and Research (Paris: UNESCO, 2023).

Deliverable. One teaching activity design with learning purpose, student verification step and access plan.

Module 4 — Student support and administration

Focus. The uses with the strongest case and least controversy: enquiry handling, timetabling support, accessibility, early warning for at-risk students — together with the data protection and false-positive risks each carries.

Lessons. 4.1 Enquiry handling and accessibility. 4.2 Early warning systems and their harms. 4.3 Data protection in student data. 4.4 Human escalation as a requirement.

Core reading. Olaf Zawacki-Richter et al., “Systematic Review of Research on Artificial Intelligence Applications in Higher Education”, International Journal of Educational Technology in Higher Education 16, article 39 (2019). Virginia Eubanks, Automating Inequality (New York: St. Martin’s Press, 2018), chapters 1–3, for the pattern of harm in eligibility and risk scoring.

Deliverable. Evaluation of one administrative use case with data protection assessment and escalation design.

Module 5 — Policy, capability and the honest position

Focus. Student-facing rules that can be applied consistently, staff development that targets supervisory competence, and public statements about the institution’s position that will not embarrass it later.

Lessons. 5.1 Student-facing rules that can be enforced. 5.2 Staff capability and supervision competence. 5.3 Consistency across faculties. 5.4 Public statements and reputational exposure.

Core reading. Neil Selwyn, Should Robots Replace Teachers? AI and the Future of Education (Cambridge: Polity Press, 2019), chapters 1 and 6. UNESCO, Guidance for Generative AI in Education and Research (2023).

Deliverable. Final submission: assessment redesign package, teaching activity, administrative evaluation and a one-page institutional position paper.

Assessment

Pass mark 70 per cent. All assessed components must be attempted. Every mark in this course is issued by a human assessor; no assessment outcome is generated automatically.

Rubric criteria

Each assessed artefact is marked against four criteria at four levels (distinction, pass with merit, pass, fail).

1. Alignment: does the redesigned assessment measure the stated learning outcome, and can that be demonstrated?
2. Feasibility: is the marking and moderation workload realistic for the actual cohort size and staffing?
3. Equity: does the design avoid disadvantaging students by access, language or disability?
4. Institutional defensibility: could the position paper be published without exposing the institution to a fair charge of overclaiming?

Reading list

Core. Phillip Dawson, Defending Assessment Security in a Digital World (Abingdon: Routledge, 2020). José Antonio Bowen & C. Edward Watson, Teaching with AI (Baltimore: Johns Hopkins University Press, 2024). John Biggs & Catherine Tang, Teaching for Quality Learning at University, 4th edition (Maidenhead: Open University Press, 2011).

Peer-reviewed. Olaf Zawacki-Richter et al., “Systematic Review of Research on Artificial Intelligence Applications in Higher Education”, International Journal of Educational Technology in Higher Education 16 (2019). David J. Nicol & Debra Macfarlane-Dick, “Formative Assessment and Self-Regulated Learning”, Studies in Higher Education 31, no. 2 (2006).

Critical and policy. Neil Selwyn, Should Robots Replace Teachers? (Cambridge: Polity Press, 2019). Wayne Holmes, Maya Bialik & Charles Fadel, Artificial Intelligence in Education: Promises and Implications for Teaching and Learning (Boston: Center for Curriculum Redesign, 2019). UNESCO, Guidance for Generative AI in Education and Research (Paris: UNESCO, 2023).

All items are published works identifiable by author, title and publisher. Learners obtain them through an institutional library or the publisher. The Academy does not distribute copyrighted texts.

Academic integrity and use of AI

Generative tools may be used in producing assessed work under three conditions. Use must be disclosed in a short statement appended to each submission, naming the tool and the task it performed. Any factual or technical claim originating from a generative tool must be verified against a citable source before it enters assessed work, and the verification must be evidenced. The analytical judgement in each artefact must be the learner’s own and must be defensible in a short follow-up. The redesigned assessment must be one you are willing to deploy. Designs that solve the integrity problem by making the task trivial will not pass the alignment criterion.

Instructor: pending owner confirmation. Pricing: pending owner approval. Reference list verified against publisher records; any later addition is marked for verification before publication.