How can researchers, PhD students, and academics use AI tools in their workflows without crossing ethical lines?In this episode, Jeroen Schreel sits down with Faheem Ullah, an Assistant Professor in Computer Science at the University of Adelaide, to dismantle the hype and provide a technical roadmap for the modern academic. Faheem breaks down the hierarchical differences between AI, Machine Learning, and Deep Learning, while offering a pragmatic framework for using these tools without compromising your academic integrity.

(00:00) Intro

(00:28) Ad

(01:43) The Fascinating Energy Consumption of the Human Brain

(03:06) Choosing Computer Science: Why the Future is Pervasive

(03:52) AI vs. Machine Learning vs. Deep Learning

(06:08) How Deep Learning Automates Feature Selection

(08:18) Hyperparameters vs. Parameters in Model Training

(10:14) Productivity vs. Creativity: The Two Schools of Thought

(12:14) Reducing a Week of Literature Review to One Hour

(14:18) Using Pilot Studies to Fact-Check AI Search Strings

(15:51) Summarizing 1,000 Papers: Are We Losing Research Depth?

(18:51) Listening to Research: The Future of "Reading" Papers

(20:25) Why AI-Generated Papers Can't Reach Human Quality (Yet)

(21:41) Practical AI: Using Models for Data Analysis and Visuals

(22:55) Avoiding Bias: A Cautionary Tale of AI-Generated Avatars

(24:41) The Paywall Problem: How AI Excludes Certain Research

(26:03) Specialized Tools: Moving Beyond ChatGPT

(28:05) The End of Cheap AI? $600 Billion Data Centers

(30:47) Will OpenAI Include Ads in ChatGPT?

(33:28) The 80% Rule: Responsibility and Fact-Checking

(35:55) Why Traditional Essay Assignments are Now Obsolete

(38:03) The AI Feedback Loop: Training Models on AI-Generated Data

(39:20) Why Social Media Algorithms Suppress AI Content

(41:20) Using AI for Research Infographics and Communication

Relevant papers:- Shumailov et al., 2024. AI models collapse when trained on recursively generated data. Nature, 631: pp. 755-759; https://doi.org/10.1038/s41586-024-07566-y- Cao et al., 2025. Automation of Systematic Reviews with Large Language Models. Preprint; https://doi.org/10.1101/2025.06.13.25329541- Han et al., 2024. Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview. Applied Sciences, 14: Article 9103; https://doi.org/10.3390/app14199103- Galli et al., 2025. Large Language Models in Systematic Review Screening: Opportunities, Challenges, and Methodological Considerations. Information, 16: Article 378; https://doi.org/10.3390/info16050378- Bittle and El-Gayar, 2025. Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda. Information, 16: Article 296; https://doi.org/10.3390/info16040296- Francis et al., 2025. Generative AI in Higher Education: Balancing Innovation and Integrity. British Journal of Biomedical Science, 81: Article 14048; https://doi.org/10.3389/bjbs.2024.14048- Luccioni et al., 2024. Power Hungry Processing: Watts Driving the Cost of AI Deployment? Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency: pp. 85-99; https://doi.org/10.1145/3630106.3658542- Ullah et al., 2026. Towards Ethical AI Adoption in Academic Research: Insights from a Systematic Literature Review. Proceedings of the AAAI Summer Symposium Series, 9: pp. 117-125; https://doi.org/10.1609/aaaiss.v9i1.42913- Kirchenbauer et al., 2024. A Watermark for Large Language Models. Preprint; https://doi.org/10.48550/arXiv.2301.10226

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