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GenAI Productivity: Ideas to project proposal 3

Produced using Google Gemini

In two previous posts I looked at using Generative AI to start producing  project ideas and refining one to be the start of the proposal, Previous blog posts
One of the most significant cognitive hurdles for students is the transition from a high-level area of interest to a rigorously defined project specification. This "blank page" problem often leads to poorly scoped projects or a lack of alignment with current academic literature.

Building on my previous experiments in prompt-mediated productivity, this post examines a structured workflow for using Large Language Models (LLMs)—specifically GPT-4o—to scaffold the development of a BSc Software Engineering project proposal.

The Methodology: Multi-Stage Prompt Scaffolding

Rather than requesting a single output, the workflow utilizes a recursive prompting strategy. This mirrors the iterative refinement process we expect from students, but accelerates the initial drafting phase.

Stage 1: Broad Ideation and Constraint Mapping

The initial objective is to map the "possibility space" within a specific domain, such as Ambient Assisted Living (AAL). By providing the LLM with a specific student persona and technical constraints, we ensure the output is calibrated to the appropriate academic level.

The Instructional Prompt:

"Generate five distinct project ideas suitable for a BSc Software Engineering student specializing in Ambient Assisted Living. The projects must emphasize complex programming logic. For each, provide: a 100-word abstract, a 200-word academic justification, core functional goals, and a bill of materials (physical resources)."

Stage 2: Deepening Rigor and Academic Grounding

Once a candidate project is selected—in this case, a Voice-Controlled Personal Assistant (VCPA) for Dementia Patients—the next phase involves "hardening" the proposal. This requires the LLM to integrate formal citations and expand the technical justification to meet the requirements of a formal module descriptor or proposal document.

The Refinement Prompt:

"Expand Project Idea 4 into a formal 700-word proposal. Integrate Harvard-style citations throughout the justification and description. Focus on the technical implementation (NLP, speech-to-text) and the ethical considerations of assistive technology. Include a comprehensive reference list."

Case Study: The "Cognitive Assistant" Proposal

The resulting output demonstrated a sophisticated understanding of the intersection between software engineering and healthcare informatics. Key highlights included:

  • Technical Stack Identification: Moving beyond generic "AI," the model suggested specific open-source frameworks like Rasa for dialogue management and Vosk/DeepSpeech for privacy-preserving, offline inference—a critical requirement for sensitive healthcare data.

  • Architectural Feasibility: The proposal mapped the solution onto low-cost Edge hardware (Raspberry Pi 4), providing a realistic scope for a self-funded student project.

  • Academic Context: The justification aligned the project with the IEEE's "Ethically Aligned Design" and the WHO's Global Action Plan on Dementia, providing the student with a high-level framework for their literature review.

The Critical Intervention: Human-in-the-Loop (HITL) Verification

In a computing education context, the "Human Bit" is not merely an afterthought; it is the primary learning objective. For supervisors and students, this workflow necessitates a rigorous audit phase:

  1. Citation Forensics: LLMs are prone to "hallucinating" academic sources. Students must be taught to verify every DOI and paper title against reputable databases (ACM Digital Library, IEEE Xplore).

  2. Feasibility Auditing: While the LLM can suggest an architecture, the student must verify whether a Raspberry Pi has sufficient computational capacity for real-time NLP with minimal latency.

  3. Reflective Personalisation: The AI output serves as a "minimum viable product" (MVP). The student’s role is to inject their own unique research questions and technical nuances that distinguish an automated draft from a personal contribution to the field.

Pedagogical Implications

By using GenAI as a "drafting partner," we shift the student’s labour from low-level structural drafting to high-level critical evaluation. This approach doesn't just produce a better proposal; it models the type of AI-augmented engineering workflow that is likely to become standard in  industry.

We should view these tools not as a threat to academic integrity, but as a sophisticated form of scaffolding that allows students to engage with complex project design more rapidly and deeply.


All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon
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