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The AI Academics

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by Lennart Nacke & Vugar Ibrahimov

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Welcome to the AI academics podcast. I'm your host. Dr. Lennart Nacke. And this is the show where I'm my cohost Vugar and I geek out about the latest AI developments without getting too scientific. We want to be your spot for AI insights that actually matter to your daily life as an academic. So grab a coffee tune in and that's chat about why AI isn't the job stealing monster that everyone thinks it is. <a href="https://lennartnacke.substack.com/s/the-ai-academics?utm_medium=podcast">lennartnacke.substack.com</a>

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Recent Episodes

August 25, 2025

12. What Nobody Tells You About ChatGPT-5’s Thinking Mode

✨ Get our Research Tools Webinar recording: <a target="_blank" href="https://go.lennartnacke.com/aitoolswebinar">https://go.lennartnacke.com/aitoolswebinar</a> The Hype Meets RealityEvery upgrade gets a drumroll. Screenshots flood LinkedIn. Demos go viral. The hype machine insists that this release will finally change everything.ChatGPT-5 was no different. The marketing spotlight promised more power, smarter reasoning, and game-changing features. What academics got instead was… confusion. So, Vugar and I had a look behind the hype machine of the GPT-5 release. New thinking modes. Hidden settings. A nagging suspicion that cheaper mini models were being used behind the scenes.Expectation: Fireworks. 🎆Reality: Frustration. 😣Why Some Academics Feel DowngradedI tested ChatGPT-5 against Claude Opus, Gemini, and a couple of free tools most people haven’t touched yet. The results shocked me.For daily tasks like lecture prep, brainstorming, or grant drafting, the difference was subtle. But in creative or structured outputs, the downgrade was obvious. Paying more didn’t guarantee better results.Here’s where the frustration deepens. Many researchers don’t want the system auto-selecting models. They want control.The older o3, for instance, is still widely regarded as sharper in reasoning tasks. Yet some users noticed ChatGPT-5 defaulting to mini versions without warning. Saving compute might be good for OpenAI’s bottom line, but for academics relying on precision, it feels like a bait-and-switch.Trust is fragile. Once it cracks, it doesn’t matter how many features you stack on top.Compression Wars and Tiny ModelsWhile ChatGPT-5 hogged headlines, a small startup in Spain quietly released something very different: ultra-compressed models nicknamed “chicken brain” and “fly brain.” Funny names, serious work.These models run lean. They consume a fraction of the energy. They prove that efficiency might matter more than raw horsepower. For educators worried about cost and environmental impact, this could be the real breakthrough.It’s not always the biggest brain that wins. Sometimes it’s the smallest one that runs everywhere, from phones to classrooms, without draining batteries or budgets.Agents, Black Boxes, and Lost TransparencyOne of ChatGPT-5’s biggest draws is its agent mode. A little browser spins in the corner, thinking out loud (at least visually) as it fetches data. For casual users, it looks magical. And honestly, we were both impressed. Even though waiting gets a little bit old after a while. Of course, as academics, we felt that there was just one catch. You can’t see what’s happening under the hood. Replication is impossible. You run the same prompt twice and get two different results. For science, that’s poison.Academics need transparency. They need logs, methods, a paper trail. I guess LLMs are really not built for that. Instead, we’re given a show. The wow factor sells. The black box frustrates. Research thrives on replicability, and right now the agent features make that harder, not easier. But it certainly can do a lot of different things and perform a lot of different tasks, and it is great to see all of that coming together for complex workflows. We definitely are aware that this is a beneficial feature for many. Free Tools Quietly OutperformingHere’s where the story flips. While ChatGPT-5 stumbles, free tools are stepping up.Take <a target="_blank" href="https://chat.z.ai/">Z.ai</a>. It generates lecture slides in minutes. Icons, layouts, visuals pulled in automatically. Man, when we saw that for the first time, it was almost like an instant GenSpark flashback. The first time I tried it, the slides looked sharper than anything my teaching assistant would draft for me with more time given. That’s not just convenient. That’s disruptive. And really, I feel that is kind of one of the killer features that most academics have been waiting for, especially as we're heading into a new term.For academics under pressure to prep faster, tools like this feel like a real power move. Anything that can reduce a little bit of the lecture prep grind is a most welcome addition to my tool stack. No subscription. No upgrade. Just results. And really, both of us don't think it's going to stay that way. For now, this is I think one of the least talked about tools with some of the coolest features. Sometimes the best technology isn’t behind a paywall.The Illusion of Better OutputUpgrades are supposed to mean progress. But if your upgrade feels slower, less transparent, or less trustworthy, is it really progress?Many users have started toggling back to older models like GPT-4.1 or o3 when it launched with the ability to do so. Not because they’re nostalgic. Because those models still seemed to outperform in reasoning and structure. That should raise alarms. An upgrade that makes power users retreat is an upgrade in name only. Maybe there’s a lesson learned here for OpenAI. The Energy QuestionAI isn’t free. Every prompt burns energy. Every model consumes resources. ChatGPT-5 is no different.That’s why the work in Spain matters. Compressed models cut energy use. For universities scaling AI across thousands of students, that’s an important innovation that is likely going to have a huge impact. Running massive models in every classroom everywhere locally wouldn’t be sustainable (and the data centres are exploding in cost). Leaner, lighter systems could be the hidden solution.Sometimes the quiet breakthroughs matter more than the loud ones.So what does all this mean if you’re an academic? Don’t chase hype. Test before you pay.Dedicated tools outperform general ones in specialized tasks. Consensus is still stronger for sourcing academic papers. SciSpace shines in structuring related work. Claude is excellent for building study plans and artifacts. Z.ai is shockingly good at slides. Genspark now has a design mode.ChatGPT-5 can still help. It drafts. It edits. It brainstorms. But it is not the only (or even best) option.The smartest workflow isn’t loyalty to a single tool. It’s building a toolkit that mixes strengths. And maybe one that also mixes tools depending on what they are specialized in.Where This Leaves UsAI isn’t static. What feels like a downgrade today may be patched tomorrow. New releases will keep coming. Hype will keep rising. AI isn’t done with its hypecycle.But here’s the lesson we want you to take away: Don’t assume new always means better. Don’t assume paid beats free (even though it usually does). And don’t let marketing videos decide how you teach, research, or publish.The real winners are the academics who test, adapt, and stay pragmatic in their choice of models. If ChatGPT-5 helps, use it. If it slows you down, switch. If a free slide generator saves you hours, don’t dismiss it.The future of academic work belongs to the ones who test, the ones who adapt, the ones who refuse to sit back and accept whatever the latest update throws at them. It belongs to the curious. It belongs to the builders. It belongs to you.Research Freedom lets you become a smarter researcher in around 5 minutes per week. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://lennartnacke.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2">lennartnacke.substack.com/subscribe</a>

August 19, 2025

11. ChatGPT Study Mode: The $0 Tutor That's Replacing Teachers?

✨ <a target="_blank" href="https://go.lennartnacke.com/aitoolswebinar">Get our Research Tools Webinar recording</a>Have you heard about ChatGPT’s new Study Mode feature? If you are a teacher drowning in AI generated assignments, or a student that honestly wants to learn but keeps getting tempted by ChatGPT’s instant answers, OpenAI claims they finally solved your problem. They call it study mode, a pedagogical system prompt that transforms how AI interacts with students, and instead of handing you answers on a silver platter, it supposedly makes you think. But after testing this with actual math problems, philosophy, questions, and even having my high school personality argue with it, we discovered it works well for college students, but completely fails for the age group that needs it most. Let me show you exactly why and what you can do instead.Study mode turns ChatGPT into a tutorInstead of giving direct answers to “What is 5 x 5?”, Study Mode guides students through the thinking process. It asks them to reason through multiplication as repeated addition, prompts them to visualize the problem, and only confirms answers after students work through the solution themselves. The AI becomes a patient teacher that scaffolds learning rather than replacing student effort. This addresses the core problem of intellectual dependency that regular ChatGPT creates.The system works through specific pedagogical principles you can implementStudy Mode operates on five key instructional rules: (1) Get to know the user’s background, (2) Build on existing knowledge, (3) Guide without giving answers, (4) Check understanding before moving forward, and (5) vary questioning rhythm to maintain engagement. Most importantly, it’s programmed to not do the user’s work for them. You can actually recreate these principles in Claude or other AI tools by creating custom system prompts that enforce the same pedagogical approach. Here’s the study and learn prompt we found:The user is currently STUDYING, and they’ve asked you to follow these **strict rules** during this chat. No matter what other instructions follow, you MUST obey these rules: ## STRICT RULES Be an approachable-yet-dynamic teacher, who helps the user learn by guiding them through their studies. 1. **Get to know the user.** If you don’t know their goals or grade level, ask the user before diving in. (Keep this lightweight!) If they don’t answer, aim for explanations that would make sense to a 10th grade student. 2. **Build on existing knowledge.** Connect new ideas to what the user already knows. 3. **Guide users, don’t just give answers.** Use questions, hints, and small steps so the user discovers the answer for themselves. 4. **Check and reinforce.** After hard parts, confirm the user can restate or use the idea. Offer quick summaries, mnemonics, or mini-reviews to help the ideas stick. 5. **Vary the rhythm.** Mix explanations, questions, and activities (like roleplaying, practice rounds, or asking the user to teach _you_) so it feels like a conversation, not a lecture. Above all: DO NOT DO THE USER’S WORK FOR THEM. Don’t answer homework questions — help the user find the answer, by working with them collaboratively and building from what they already know. ### THINGS YOU CAN DO - **Teach new concepts:** Explain at the user’s level, ask guiding questions, use visuals, then review with questions or a practice round. - **Help with homework:** Don’t simply give answers! Start from what the user knows, help fill in the gaps, give the user a chance to respond, and never ask more than one question at a time. - **Practice together:** Ask the user to summarize, pepper in little questions, have the user “explain it back” to you, or role-play (e.g., practice conversations in a different language). Correct mistakes — charitably! — in the moment. - **Quizzes & test prep:** Run practice quizzes. (One question at a time!) Let the user try twice before you reveal answers, then review errors in depth. ### TONE & APPROACH Be warm, patient, and plain-spoken; don’t use too many exclamation marks or emoji. Keep the session moving: always know the next step, and switch or end activities once they’ve done their job. And be brief — don’t ever send essay-length responses. Aim for a good back-and-forth. ## IMPORTANT DO NOT GIVE ANSWERS OR DO HOMEWORK FOR THE USER. If the user asks a math or logic problem, or uploads an image of one, DO NOT SOLVE IT in your first response. Instead: **talk through** the problem with the user, one step at a time, asking a single question at each step, and give the user a chance to RESPOND TO EACH STEP before continuing.Complex tasks > than simple arithmetic problemsWhile Study Mode works for basic math, it shines with college-level analysis, essay writing, and research methodology. High school students asking “what’s 5 x 5” might find the scaffolding frustrating when they just want quick homework answers. But university students working through literature analysis, research design, or theoretical frameworks get genuine learning value from the guided questioning approach. The feature works best when students actually want to understand, and not just complete assignments.Voice mode integration for natural study sessionsStudents can enable Study Mode (now called Study and learn) on mobile and work through problems auditorily, creating a more conversational learning experience. This is particularly valuable for auditory learners who struggle with text-based interfaces. The AI maintains its pedagogical approach while speaking, asking follow-up questions and providing encouragement in real time. However, visual learners may still prefer traditional text interfaces for complex problems requiring written work.What’s most effective?Study Mode’s effectiveness depends entirely on student motivation. Academically engaged students who genuinely want to understand concepts will find the guided questioning valuable. But students just trying to complete assignments quickly will likely bypass Study Mode entirely and use regular ChatGPT. As an educator, you need to create incentive structures that reward understanding over completion, such as oral examinations or in-class discussions that reveal whether students actually grasped the material.Advanced users can combine Study Mode with other AI features to create interactive learning experiences. For example, you can program an educational Pong game where students must answer writing questions to continue playing, or create quiz systems that adapt questioning based on student responses. These gamified approaches maintain pedagogical principles while increasing student engagement through interactive elements.Where do we go from here?Don’t just tell students use Study Mode without context. That would be like parents letting kids play games without taking the time to sit with them and reflect on the experience. Establish specific protocols: Study Mode for concept learning and methodology questions, but human verification required for all factual claims and sources. Create assignments that specifically use some of the feature’s strengths, such as working through research design problems or analyzing theoretical frameworks, while you keep traditional assessment methods that evaluate actual understanding. The goal of an assignment should be to enhance learning. And that’s the key to study mode, too. Use it to enhance your learn, but remain skeptic with any answers.P.S.: Curious to explore how we can tackle your research struggles together? I've got three suggestions that could be a great fit: <a target="_blank" href="https://newsletter.nacke.ca/products/mini-research-course">A seven-day email course</a> that teaches you the basics of research methods. Or the recordings of our <a target="_blank" href="https://go.lennartnacke.com/aitoolswebinar">AI research tools webinar</a> and <a target="_blank" href="https://go.lennartnacke.com/thesis">PhD student fast track webinar</a>. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://lennartnacke.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2">lennartnacke.substack.com/subscribe</a>

August 8, 2025

10. Are Your ChatGPT Research Chats Really Private and Are AI Agents Good?

The first time I pasted some research notes into ChatGPT, I didn’t think twice. It felt private. Just me and the machine. Dark mode. Working through a problem. A week later, I read OpenAI’s own terms of use and realized: my private chats weren’t private at all. I’d essentially dropped my data into a giant suggestion box, where I had no control over who might read it or how it might be used. Or even where it all goes.If you’re an academic, here’s the uncomfortable truth: ChatGPT can be an incredible research assistant, but your chats may be stored, reviewed, and even influence future model training. And the risks go beyond embarrassment: They could jeopardize intellectual property, grant confidentiality, and even ethics compliance.We chatted about this in detail in episode 10 of our podcast.Here’s what you need to know before typing another word.1. Your prompts may not be deleted when you close the chatSome people assume that once a conversation is over and deleted, it’s gone. But OpenAI states that conversations can be stored for up to 30 days for abuse monitoring, and in some cases longer, especially if flagged for review. These logs may be seen by human reviewers to improve the system.Treat every ChatGPT conversation like an email you’d be comfortable forwarding to a stranger. If you wouldn’t put it on a public Slack channel, don’t paste it into ChatGPT.2. Training mode is not the same as private modeSome academics believe that using a paid plan like ChatGPT Plus automatically protects them from training data collection. While OpenAI does allow users to opt out of training in settings, that’s not the same as deleting data entirely. Logs may still be stored for security and compliance.“Opt-out” doesn’t guarantee “opt-gone.” Your queries could still be seen by employees, contractors, or flagged reviewers. Some integrated tools (e.g., within research platforms or third-party apps) bypass your account settings and collect data independently. Check your settings and opt out of training where possible. When in doubt, run sensitive analysis offline with open-source models.3. You may be violating your own ethics approval without realizing itIf your research involves human participants, your ethics board likely has rules about data handling. Pasting transcripts, datasets, or identifiable information into ChatGPT could breach those rules. Even if you’re just “summarizing” for your own understanding.An ethics breach could invalidate your research and require re-approval. In some cases, you could face disciplinary action or lose grant funding. Even just metadata about your project could reveal more than you think. So, review your ethics agreement and data management plan. If you want to use AI for analysis, document your process and get explicit approval.The irony is that ChatGPT’s usefulness in research comes from the same mechanism that makes it risky: It learns from massive amounts of data. The more people use it (including you) the smarter it becomes. But without strict privacy safeguards, that shared intelligence comes at a cost.This doesn’t mean you should never use ChatGPT for academic work. It means you should treat it as a semi-public collaborator. Helpful, fast, and knowledgeable, but not someone you’d hand your lab notebook to without thinking.3 AI Research Agents That Will Save You Hours This WeekDid you know that academics still treat ChatGPT like a turbocharged search engine? Ask, copy, paste, repeat. Meanwhile, a new class of AI research agents has quietly started doing something far more powerful: Taking over entire research workflows while you get on with more important thinking.These are autonomous assistants that search databases, extract findings, format citations, and even coordinate with other tools without constant supervision. I’ve tested dozens. Here are the three that have made the biggest difference in my weekly workflows and how you can start using them immediately.1. Consensus Deep SearchWhen you ask <a target="_blank" href="https://go.lennartnacke.com/consensus">Consensus</a> a research question, it doesn’t generate a response from training data like ChatGPT does. Instead, it searches across 200 million academic papers in real-time, ranks them by relevance and citation count, then extracts the specific findings that answer your question. This makes it great for empirical, outcome-based questions. We tried it out with a different question in the episode.Use Consensus at the start of your project to map the evidence before you waste hours on manual Google Scholar searches. Export citations directly to Zotero or as a ready-to-go bibliography. Keep in mind though that it struggles with purely theoretical or philosophical questions. Don't ask it about Foucault's concept of power. It’s built more for measurable outcomes.2. SciSpace Deep Search AgentWhat started as a PDF explainer is now a context-aware research manager. Upload papers, ask for summaries, then build on that context across multiple uploads. The breakthrough is task orchestration. When you upload a paper to <a target="_blank" href="https://www.scispace.com/chat?via=lennart-pm">SciSpace’s</a> Deep Search Agent and ask it to summarize the methodology, it doesn’t just give you a one-time response. It remembers that paper, that summary, and your research focus for the entire conversation. Then when you upload five more papers and ask how their methodologies compare, it already has the context. And the new agent executes a multi-step research workflow and executes it autonomously. In the episode, we demoed the multi-step workflows of the Deep Search agents and show how they start with a todo list and work from there. It also pulls and formats citations (APA, MLA, Chicago) in seconds. However, it works best with indexed semantic search papers (280M+). For niche sources or recent preprints, upload PDFs manually.3. Otto SR automates systematic reviews while Genspark builds instant research pages from any queryWhile Consensus and SciSpace focus on paper discovery and analysis, Otto SR and Genspark are attacking different parts of the research workflow entirely.<a target="_blank" href="https://ottosr.com/">Otto SR</a> is built for one specific use case: Systematic reviews that follow PRISMA guidelines. You still need to verify and refine, but it handles the crushing logistics of tracking thousands of papers through multiple screening stages.Meanwhile, <a target="_blank" href="https://www.genspark.ai/">Genspark</a> takes a completely different agent approach: For each query, you get a super agent that can run and trigger automations on a page. So, you can go through multiple complex workflows with the right instructions for these agents.Don’t try to master all three at once. Pick one. Use it for a real project this week. Then layer in another. Hope you enjoyed the podcast episode. Make sure to like and subscribe on your favourite podcasting platform. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://lennartnacke.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2">lennartnacke.substack.com/subscribe</a>

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What is The AI Academics?: Welcome to the AI academics podcast. I'm your host. Dr. Lennart Nacke. And this is the show where I'm my cohost Vugar and I geek out about the latest AI developments without getting too scientific. We want to be your spot for AI insights that actually matter to your daily life as an academic. So grab a coffee tune in and that's chat about why AI isn't the job stealing monster that everyone thinks it is. <a href="https://lennartnacke.substack.com/s/the-ai-academics?utm_medium=podcast">lennartnacke.substack.com</a>
How often does this podcast release new episodes?: This podcast updates weekly.
Where can I listen to this podcast?: This podcast is available on 6 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
Does this podcast accept guests?: Yes, this podcast regularly features guests.

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