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

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

 

Using generative AI to augment part-time Housing Studies 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 be a powerful learning aid for part-time Housing Studies students working on dissertations, but only when used to augment—not replace—human judgment, critical engagement, and scholarly rigour. Below are four ways this practice can be conducive to effective learning, followed by four ways it can be ineffective, harmful, or academically dishonest.

Conducive to effective learning

1. Structured literature scoping and gap identification

AI tools can rapidly synthesise large volumes of housing policy reports, planning documents, and academic papers to help students map the field, identify recurring themes, and spot under-researched areas. When students then verify and critique these AI-generated summaries against primary sources, they develop stronger information literacy and sharper research questions.pmc.ncbi.nlm.nih+2

2. Iterative drafting and language refinement

For non-native English speakers or time-constrained part-time students, generative AI can suggest alternative phrasings, improve sentence clarity, and help structure arguments without generating whole sections. This supports learning by letting students focus on argument logic and evidence while refining academic writing skills through comparison and revision.ru+1

3. Methodological scaffolding and protocol design

AI can help students draft interview guides, survey instruments, or coding frameworks by generating examples aligned with stated research aims. When students adapt these templates to their specific housing context (e.g., public rental housing in Hong Kong), they learn methodological design principles while avoiding the pitfalls of starting from scratch.pmc.ncbi.nlm.nih+1

4. Reflective feedback and self-assessment

Students can use AI to simulate examiner questions, generate counter-arguments to their thesis, or highlight logical gaps in their draft chapters. Engaging critically with this feedback—accepting, rejecting, or refining it—builds metacognitive skills and prepares students for viva or defence scenarios.frontiersin

Ineffective, harmful, or academically dishonest practices

1. Outsourcing critical reading and synthesis

Using AI to summarise papers or generate literature review content without engaging with the original texts undermines deep learning and critical analysis. This “cognitive offloading” leaves students unable to defend sources, evaluate methodologies, or identify biases—core competencies in Housing Studies research.pmc.ncbi.nlm.nih+3

2. Fabricated or unverified citations and data

Generative AI is prone to “hallucinating” references, statistics, or case studies that appear plausible but are entirely fictitious. Submitting such content without verification constitutes academic misconduct and risks propagating misinformation in the scholarly record.pmc.ncbi.nlm.nih+4

3. Ghostwriting entire chapters or sections

Prompting AI to write full dissertation chapters—especially without disclosure—violates academic integrity policies and deprives students of the learning that comes from constructing arguments, analysing data, and iterating drafts. This practice also erodes the credibility of the degree and the student’s own professional development.aisel.aisnet+2

4. Undisclosed use and lack of transparency

Failing to declare AI assistance in methodology, writing, or data processing breaches emerging academic norms and journal policies. Transparency is essential for reproducibility, peer review, and maintaining trust in research—particularly in policy-relevant fields like housing studies where findings may inform public decisions.files.eric.ed+3

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Key principle: Treat generative AI as a “research assistant” that accelerates routine tasks and expands possibilities, not as a substitute for your intellectual labour. Always verify outputs, disclose usage per institutional guidelines, and ensure that every AI-augmented step ultimately strengthens your own understanding and scholarly contribution.

 

A collection of blog notes on using chatgpt for research purpose.