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GenAI Productivity: Ideas to project proposal ideas from Google Scholar

From Google Scholar to Project Ideas or Using AI to Map the Future of Your Research



Generated as well by Google Gemini






Have you ever looked at a researcher's Google Scholar profile and felt overwhelmed by the lists of citations and technical titles? Whether you are a student looking for a dissertation topic or a professional trying to understand a collaborator's expertise, "connecting the dots" can be a challenge.

In my latest experiment, I decided to see if Google Gemini could bridge that gap. I gave it a challenge: Look at my own research profile and design 10 compelling project ideas for a final-year student.


Prompt used:  "Using this as a starting point https://scholar.google.com/citations?user=ghQedZAAAAAJ&hl=en from the research here provide 10 project ideas suitable for a final year Computer Science student project with this supervisor. For each provide title, 100 word summary, possible outcomes"


The "Ego Ride" with Unexpected Results

I’ll admit, there was a bit of vanity involved in using my own profile—but the results were genuinely surprising. Gemini didn’t just list titles; it performed a Deep Research report that I wasn’t expecting.

It analysed my most cited papers, identified my core strengths, and—most impressively—mapped them against 2024–2025 technology trends. It even "promoted" me to Professor (a hallucination I’m happy to live with!).


And yes, it did also produce ten project ideas :-). An extract of one is shown below .

"Project 10: Robotics Simulation Platform for Experiential Learning of AI and Multi-Agent Systems

This project proposes developing an interactive robotics simulation platform designed for experiential learning of AI algorithms, particularly focusing on multi-agent systems and problem-solving. Inspired by Professor Turner's interest in "Robotics within the teaching of problem-Solving" and the trend of "Physical AI" where robots train in virtual environments, the platform would allow students to design, test, and visualise AI agents controlling virtual robots in various problem-solving scenarios (e.g., maze navigation, resource gathering, cooperative tasks).  

Possible Outcomes:

  • A 3D simulation environment (e.g., using Unity, Gazebo, or a custom engine) capable of rendering multiple robots and dynamic obstacles.

  • API for students to implement and integrate their own AI algorithms (e.g., pathfinding, reinforcement learning, genetic algorithms).

  • Pre-built problem-solving scenarios (e.g., multi-robot search and rescue, collaborative assembly).

  • Visualization tools for debugging AI behavior and analyzing simulation metrics.

  • User interface for scenario creation and parameter tuning.

Underlying Trends, Thematic Links, and Implications: This project directly builds on Professor Turner's "Teaching and Learning" area, specifically "Robotics within the teaching of problem-Solving" and "Problem Solving and Creativity for Undergraduate Engineers".  

Professor Turner's work emphasizes the process of problem-solving and creativity. Traditional Computer Science education often involves theoretical study and coding exercises. The progression here is that a robotics simulation platform offers a powerful "experiential learning" environment, allowing students to directly observe the real-time consequences of their AI algorithms in a dynamic, visual context. This bridges the gap between abstract theory and practical application, reinforcing understanding of concepts like path planning , multi-agent coordination, and the nuances of AI algorithm design. This approach makes the learning process more engaging and effective, aligning with the "Physical AI" trend where robots train in virtual environments.  

Real-world robotics experiments are often expensive, time-consuming, and prone to hardware failures. The "Physical AI" trend highlights the value of virtual environments for training and experimentation. A critical consideration is that a robust simulation platform can provide students with access to complex, large-scale, and even dangerous multi-agent scenarios that would be impossible or impractical to replicate in a physical lab. This scalability allows for a much broader range of educational experiences, from simple maze solving to simulating complex factory automation or disaster response scenarios, all without the physical constraints. This effectively democratizes access to advanced robotics and AI experimentation, fostering innovation among a wider student body.  "


The Power of "Joining the Dots"

The AI identified four key pillars of my work and suggested how they could evolve:


Professor Turner's Primary Research Area

Relevant Publications/Keywords

Current CS Trends (2024-2025)

Potential Synergy/Project Direction

Artificial Intelligence (AI) and Robotics

AI, robots, genetic algorithms, Probabilistic Multi Robot Path Planning  

AI in robotics (autonomous systems, physical AI, cobots), Reinforcement Learning (RL) for autonomous systems  

Adaptive robot control, multi-agent systems, AI-driven simulation for robotics

Networking and Communication Systems

SDN, WebRTC, V2V Communication, Routing algorithm optimization, Reinforcement learning-based routing  

SDN with AI/5G/IoT, WebRTC with AI/AR/VR, V2V for road safety/traffic management, RL for networking  

Intelligent network management, real-time multimedia communication enhancements, smart vehicular networks

Biomedical and Health Applications

Optical glucose detector, Modelling chronic pain, Evoked potentials  

AI-powered diagnostics, personalized medicine, predictive analytics for patient care, remote patient monitoring  

AI for medical data analysis, smart health monitoring systems, advanced diagnostic tools

Teaching and Learning / Problem Solving

Teaching and Learning, problem-solving, creativity, Robots in problem-solving  

AI agents, intelligent automation, data foundation for AI in education  

AI-driven educational tools, interactive learning platforms for complex problem-solving

Why This Matters (Beyond the Classroom)

What started as a tool for students turned into a powerful strategic analysis for myself. It highlighted:

  • Interdisciplinary Links: How my work in robotics could influence my work in healthcare.

  • Future Directions: Identifying "Physical AI" as a bridge between my teaching and my technical research.

  • Instant Portability: The entire report can be exported directly to Google Docs, making it ready to share with students or colleagues in seconds.




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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