A note on appropriate and inappropriate employment of generative AI to support MBA students’ doing dissertation projects

A note on appropriate and inappropriate employment of generative AI to support MBA students’ doing dissertation projects

Using generative AI to augment part-time MBA students' learning when doing their dissertation projects is a common practice. Suggest 4 ways of this practice as conductive to effective learning and suggest 4 ways that are ineffective, harmful to learning and/ or academic dishonest behaviour.

Generative AI can support effective learning in a part-time MBA dissertation when it acts as a learning partner rather than a substitute for the student’s research, judgement and writing. Its educational value is greatest when the student evaluates, verifies and improves the output; uncritical reliance can weaken independent learning and critical thinking.link.springer+1

Conductive to effective learning

Practice	How it supports learning	Appropriate student responsibility
1. Brainstorming and refining the research problem	After developing an initial idea, the student can ask AI to suggest alternative perspectives, theoretical lenses, variables or research questions. This can expose overlooked possibilities and help narrow a broad managerial problem into a feasible dissertation question.	Compare suggestions with business literature, discuss them with the supervisor and formulate the final research question independently.
2. Using AI as a Socratic tutor	The student can ask AI to explain difficult concepts—such as institutional theory, thematic analysis, sampling, validity or regression—in different ways, or to question their understanding. This is particularly useful for part-time MBA students balancing employment, study and family commitments.	Treat explanations as provisional; verify definitions and methodological guidance against textbooks, peer-reviewed sources and supervisor feedback.
3. Critiquing arguments and testing assumptions	The student can provide their own proposed framework, interview questions or interpretation of findings and ask AI to identify logical gaps, rival explanations, possible bias or counterarguments. This encourages reflection and analytical questioning rather than passive acceptance.	Decide which criticisms are valid and support revisions with evidence. AI should not make the final theoretical or managerial judgement.
4. Supporting organization, analysis and revision	AI can help create a project plan, organise themes from researcher-analysed material, suggest coding structures, generate draft code or improve clarity, grammar and coherence. Guidance for dissertation projects identifies ideation, project planning, data-analysis support, outlining and proofreading as possible uses, subject to oversight. blogs.qub.ac	Maintain control of the research process, check code and interpretations, protect confidential data, and disclose the tool’s use where required by the institution.

Ineffective, harmful or dishonest

Practice	Why it is problematic
1. Asking AI to produce the dissertation or substantial sections	This replaces the learning activity—reading, synthesising, reasoning and writing—with outsourced text production. It may also breach academic-integrity rules if AI-generated work is presented as the student’s own. Guidance for graduate research commonly distinguishes permitted support, such as organising ideas and correcting minor errors, from generating or rewriting substantive portions of a dissertation. gradschool.fiu
2. Accepting fabricated or unverified sources and claims	Generative AI can produce plausible but nonexistent references, inaccurate quotations, distorted article summaries and unsupported statistics. Including these in a literature review or analysis can undermine the dissertation’s validity and constitute research misconduct if the student knowingly submits them. AI outputs should be checked against the original scholarly source. link.springer+1
3. Uploading confidential research or business data	Submitting identifiable interview transcripts, customer information, proprietary sales data or company documents to an external AI service may breach privacy, confidentiality, research-ethics or data-protection obligations. It can also damage the trust of participants and the student’s organisation. Sensitive material should be anonymised or kept out of the tool unless explicitly authorised. gradschool.fiu
4. Using AI to generate research findings or conceal the student’s contribution	Asking AI to invent survey responses, manipulate results, create interview quotations, select a preferred interpretation or write conclusions unsupported by the data is academically dishonest and may amount to data fabrication or falsification. Even less extreme overuse can reduce knowledge acquisition, critical thinking and problem-solving ability when the student follows ready-made answers instead of engaging with the research process. sciencedirect+2

Practical boundary

A useful rule is: the student may delegate routine assistance, but not intellectual ownership. The student should personally select and justify the research question, evaluate the literature, make methodological decisions, analyse evidence, draw conclusions and write the final argument.

The exact boundary should also follow the MBA programme’s policy and supervisor’s instructions. A transparent AI-use record can state the tool, date, purpose, prompts or relevant outputs, how the output was verified, and what the student changed; this supports accountability without treating AI as an academic source.

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