HE-F06 — Academic Integrity and Ethics

Course Code: HE-F06  |  School: School of Higher Education  |  Cluster: Level 1 — Foundations of Academic Practice

Level: Foundation  |  Duration: 6 weeks · 24–30 learning hours  |  Language: English  |  Certificate: Professional Certificate (non-degree)  |  Format: Self-paced with AI support under human supervision

Overview

Academic integrity is usually taught as a list of prohibitions and enforced as a detection problem. Both framings fail. Prohibition lists do not transfer to unfamiliar situations, and detection arrives after the damage. Integrity is better understood as a set of professional obligations that make collective knowledge possible, and as a design problem for institutions that must make honest conduct the path of least resistance.

This course takes that approach. It examines the epistemic rationale for research and academic norms, the full range of misconduct from fabrication to salami publication to undisclosed conflict of interest, authorship and credit disputes, plagiarism in its several distinct forms, and the institutional systems that prevent, detect and adjudicate breaches.

Generative AI is addressed directly rather than as an appendix. The course rejects both the position that all AI use is cheating and the position that disclosure alone settles the matter, and instead develops a defensible framework based on what is being assessed, what the learner must be able to defend, and what the reader is entitled to assume. Learners produce an integrity framework for a real unit or department.

Learning outcomes

On completion, a successful learner will be able to:

1. Explain the epistemic and professional rationale for academic integrity norms rather than treating them as arbitrary institutional rules.
2. Classify forms of research and academic misconduct accurately, distinguishing fabrication, falsification, plagiarism, and questionable research practices that fall short of misconduct.
3. Apply recognised authorship criteria to contested cases and justify decisions about credit, order and acknowledgement.
4. Identify and manage conflicts of interest, including funding, institutional and personal interests, through disclosure and structural measures.
5. Evaluate institutional integrity systems — education, detection, investigation, sanction and appeal — against procedural fairness standards.
6. Construct a defensible policy on the use of generative AI in a specific assessment context, with a rationale a student could understand and contest.
7. Design an integrity framework for a real unit or department, including assessment design changes rather than surveillance alone.

Who this course is for

The course is intended for academic staff, programme and department leaders, research integrity officers, graduate school and doctoral training staff, journal editors and reviewers, and postgraduate researchers. It is also suitable for professional staff who administer misconduct procedures and must apply them fairly.

Prerequisites

None. The course assumes participation in academic work as student, researcher or staff member. Access to the integrity policies of one real institution is required for the assessed work, and these are normally public.

Syllabus

Module 1 — Why integrity matters: the epistemic argument

The module establishes the foundation. Knowledge claims circulate on the assumption that reports of method and result are truthful; where that assumption fails, the entire apparatus of citation, replication and cumulative work degrades. The module examines documented cases in which fabricated findings shaped clinical or policy practice, and the cost of correction.

Lessons. 1.1 Trust as infrastructure of knowledge. 1.2 Documented cases and their consequences. 1.3 The correction record: retraction and its failures. 1.4 Norms as professional obligation.

Deliverable. Diagnostic note (800 words) analysing one documented misconduct case and its systemic causes.

Module 2 — The taxonomy of misconduct

This module distinguishes categories that are frequently conflated: fabrication, falsification and plagiarism as core misconduct; questionable research practices including selective reporting, hypothesising after results are known, and undisclosed flexibility in analysis; and administrative breaches. It examines prevalence evidence and the limits of self-report surveys.

Lessons. 2.1 Fabrication and falsification. 2.2 Plagiarism: text, data, idea and self. 2.3 Questionable practices and the grey zone. 2.4 Prevalence evidence and its uncertainty.

Deliverable. Analytical brief (1,000 words) classifying a set of borderline scenarios with justification.

Module 3 — Authorship, credit and publication ethics

The module addresses disputes over credit: authorship criteria and their application, gift, ghost and guest authorship, contributorship models, order conventions across disciplines, duplicate and salami publication, and the obligations of reviewers and editors including confidentiality and impartiality.

Lessons. 3.1 Authorship criteria applied. 3.2 Contributorship and transparency. 3.3 Duplicate, salami and redundant publication. 3.4 Reviewer and editor obligations.

Deliverable. Design artefact: an authorship and contribution policy for a research group.

Module 4 — Conflicts of interest and institutional pressure

This module examines the structural drivers of misconduct: funder influence, publication and promotion incentives, quantitative performance targets, and hierarchical pressure on junior researchers. It covers disclosure regimes, recusal, independent oversight, and the reform of incentives that reward volume over reliability.

Lessons. 4.1 Types of conflict and disclosure practice. 4.2 Funder influence and independence. 4.3 Incentives, metrics and gaming. 4.4 Power, hierarchy and the vulnerable researcher.

Deliverable. Evaluation report (1,000 words) assessing conflict-of-interest arrangements in one institution.

Module 5 — Generative AI and the boundary of one’s own work

The module builds a defensible position on AI use. It begins by asking what a given assessment is actually evidence of, then determines which uses of a model preserve that evidence and which destroy it. It covers disclosure practice, the unreliability of AI-detection tools, the risks of false accusation, and assessment redesign as the more durable response.

Lessons. 5.1 What is the assessment evidence of? 5.2 Permissible, disclosable and prohibited uses. 5.3 Detection tools and their error rates. 5.4 Redesigning assessment rather than policing it.

Deliverable. Implementation plan (1,000 words): an AI use policy for one unit with student-facing rationale.

Module 6 — Institutional systems and procedural fairness

The final module covers the machinery: preventive education that works, reporting and whistleblower protection, investigation procedure, standards of proof, proportionate sanction, appeal, and the correction of the published record. Procedural fairness is treated as a substantive requirement rather than a formality.

Lessons. 6.1 Prevention that changes behaviour. 6.2 Reporting and protecting the reporter. 6.3 Investigation, proof and proportionality. 6.4 Correcting the record.

Deliverable. Final capstone: integrity framework for a unit or department (3,000 words) with implementation sequence.

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. Conceptual accuracy. Whether categories of misconduct, authorship criteria and procedural standards are described correctly and cited to recognised codes.
2. Reasoned judgement. Whether borderline cases are analysed by applying stated principles rather than by intuition, and whether competing considerations are weighed.
3. Structural analysis. Whether the learner identifies incentive and power structures that produce misconduct, rather than attributing it solely to individual character.
4. Practical design. Whether the proposed framework is implementable, procedurally fair, proportionate, and intelligible to the students or staff it would govern.

Reading list

Core. All European Academies, The European Code of Conduct for Research Integrity, revised edition (Berlin: ALLEA, 2023).

Committee on Publication Ethics, Core Practices and associated guidance (COPE, current edition).

International Committee of Medical Journal Editors, Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals (ICMJE, current edition).

Daniele Fanelli, ‘How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data’, PLoS ONE, 4:5 (2009), e5738.

Tracey Bretag (ed.), Handbook of Academic Integrity (Singapore: Springer, 2016).

Brian C. Martinson, Melissa S. Anderson & Raymond de Vries, ‘Scientists Behaving Badly’, Nature, 435 (2005), pp. 737–738.

Academic integrity and use of AI

AI tools may be used in this course for locating literature, organising evidence, drafting non-analytical sections and criticising the learner’s own work. They may not be used to generate the analysis, the evaluation or the recommendation. Every submission carries a use-of-AI statement naming the tools used and the purpose of each use.

All citations must be verified against the primary source, and institutional facts — regulations, standards, accreditation criteria, published data — must be cited to the issuing body. Fabricated references and invented statistics result in failure and referral. Where learners write about their own institution they must not disclose confidential personnel or student data, and illustrative cases must be anonymised.