AI Research

Intent Amplified: Teaching Students How to Learn with Artificial Intelligence

Published

4 weeks ago

August 21, 2025

“You can choose to use AI to learn, or you can choose to use AI to avoid learning.”

That’s the central message of a new a new first-year philosophy course created by Joshua “Gus” Skorburg (Guelph) called, “Digital Wisdom: How to Use AI Critically and Responsibly”.

The course was prompted by Skorburg’s observation that “students get lots of vague and mixed messages about AI use, but very little sustained, hands-on demonstration of what it looks like to use AI to learn, rather than avoid learning.” He thought he should help students ask and answer the question: “What does it look like to choose to use AI to learn?”

In the following guest post, he talks about his motivation for the course, its main idea, and what he teaches his students in it.

It is a version of the first in a planned series of posts on the course for his blog/newsletter, Moving Things Around. In that series, he said by email, “I will share much of the course content and the thinking behind it, in hopes that others can use parts of my course in their own teaching, or develop a similar course in their department. I’m also very keen to get feedback from people who are less optimistic about AI than I am.”

Intent Amplified:
Teaching Students How to Learn with Artificial Intelligence
by Joshua “Gus” Skorburg

Last summer, HudZah, an undergraduate student at Waterloo, used Claude Pro, the AI from Anthropic, to build a nuclear fusor in his bedroom.

This is the kind of thing AI Natives can do, and I cannot. And like Ashlee Vance, it makes me want to weep. It also portends a crisis.

One recent study of over 1,600 faculty from 28 countries found that 40% of faculty feel that they are just beginning their AI literacy journey and only 17% are at advanced or expert level.

This Fall, how will the 83% of faculty lacking AI literacy address students who feel ripped off: expected to use AI professionally but not taught how? How will they answer students questioning why they should pay tuition when AI teaches better, faster, cheaper?

I’ve not seen many answers that students are likely to find convincing. That’s why I spent the summer developing a new first-year Philosophy course called, “Digital Wisdom: How to Use AI Critically and Responsibly”. The course’s message is simple:

You can choose to use AI to learn, or you can choose to use AI to avoid learning.

By now, everyone knows what it looks like to use AI to avoid learning, although the strategies are becoming more sophisticated and harder to detect.

The problem, as I see it, is that students get lots of vague and mixed messages about AI use, but very little sustained, hands-on demonstration of what it looks like to use AI to learn, rather than avoid learning.

So, what does it look like to choose to use AI to learn?

The Pedagogical Potential of AI

First, there’s the choice. It requires concerted, deliberate action and it doesn’t happen by default.

One method, persona prompting, is the lowest-hanging fruit here. Rather than asking for answers, have students tell the LLM things like, “You are a biology professor who specializes in making complex concepts accessible to first-year students. Explain CRISPR using Canadian agricultural examples.”

If students learn better through concrete examples, then: “Explain [concept] by providing three real-world examples from different domains, then show me how the same principle applies in each case. Quiz me at the end to test my understanding.” And so on.

By now, everyone also knows the risks of AI in education and many judge them high enough to justify AI “bans.” I don’t think total bans are feasible. Sure, we can mandate in-person exams, but does anyone honestly think students don’t use ChatGPT to prepare for them?

Reddit is full of examples of how students use AI in this way. Students I trust have told me how they’ve done so to prepare for my in-person essay exams (“upload the study guide to ChatGPT, try to memorize the outputs”). Banning AI just incentivizes unguided shadow use, where avoiding learning is more likely.

We also shouldn’t forget about the risks of not using AI. It can be very difficult for some students to ask clarification questions in large lecture halls, or to admit that they don’t understand a basic concept in front of their peers.

One of the most important features of LLMs for learning is that they are patient and non-judgmental. Students can ask as many follow-ups as they want. They can ask for explanations tailored to their learning styles, or for analogies to domains they are more familiar with. Banning AI in the classroom deprives students of these learning opportunities.

New features like ChatGPT’s Study Mode, Claude’s Learning Mode, and Gemini’s Guided Learning incorporate the above ideas with the click of a button.

Of course, it’s never so simple as clicking a button.

The Hidden Curriculum of Default AI

A big problem with today’s LLMs is that they are sycophantic: they tend to tell users what they want to hear and use flattering language that is inconducive to learning. Unfortunately, the default setting of LLMs seems to incentivize providing the illusion of learning, without the hard work of actually learning.

When AI constantly validates and flatters, it can create false confidence in weak work and prevent genuine skill development. In extreme cases, it can even contribute to psychotic breaks.

Thus, when it comes to using AI for learning (and AI use more generally) one of the most important prompting strategies is anti-personas, or telling the AI what it is NOT.

By explicitly programming against sycophancy, you make it more likely that you will get the kind of honest feedback that actually promotes learning: The kind a trusted mentor would give in private, not the polite encouragement given in public.

Here are some examples I encourage students to use in my course:

The “brutal editor” persona prompt

“You are a harsh but fair editor reviewing my work. You are NOT interested in making me feel good about my writing. You do NOT start with compliments or end with encouragement. You do NOT say things like “great job” or “you’re on the right track.” Instead, directly identify specific problems and explain why they weaken my argument. Be concise and critical.”

The “skeptical professor” persona prompt

“You are a demanding professor who has seen thousands of student papers. You are NOT impressed by basic observations or surface-level analysis. You do NOT give credit for merely attempting something. You do NOT soften criticism with praise sandwiches. Point out exactly where my thinking is shallow, where my evidence is weak, and where my logic fails. If something is genuinely good, you’ll mention it briefly, but focus on what needs improvement.” And so on.

Custom Instructions

At this point, many will object: “The temptation to just ask AI to do all the work is too great, and students won’t reliably use those prompts.”

Fair point. Students can and do choose to take shortcuts. But they can also choose to not do this, if they are shown good alternatives.

An underutilized feature in today’s LLMs is “custom instructions” (ChatGPT, Claude, Gemini). These are like “meta prompts” that automatically apply to all your conversations with an LLM.*

Here’s what I say to students in my course:

If you want to make it more likely that AI will help you learn rather than avoid learning, add custom instructions like:

- “When I ask for help with an assignment, respond with 3-4 targeted questions that will help me think through the problem myself, rather than giving me solutions. Only provide direct guidance after I’ve demonstrated my own reasoning.”
- “If I ask you to write, summarize, or analyze something for me, instead provide a structured thinking framework and ask me to work through it step-by-step, checking my reasoning at each stage.”
- “If I seem to be using you to avoid learning rather than to enhance learning, point this out.”

Interviews

A fun and thought-provoking way to write these custom instructions is to have the AI interview you, with a learning-focused prompt like this:

Please interview me to develop a set of custom instructions for [ChatGPT, Claude Gemini]. Help me create learning-focused custom instructions by asking about: (1) how I want [ChatGPT, Claude, Gemini] to support my learning process without doing the thinking for me, (2) my preferences for direct, honest communication over excessive positivity or sycophancy, (3) my background and main use cases, and (4) specific output requirements. Focus especially on understanding when I want to be challenged, corrected, or pushed to think harder rather than given easy answers. Ask follow-up questions as needed. At the end of the conversation, please draft the custom instructions for me to review.

These aren’t silver bullets. Students can always choose to override custom instructions. But they’re no less a band-aid solution than “banning” AI and driving use into the shadows.

Podcasts and Flywheels

All the examples so far are strategies I give to students. But here’s one of my personal favorites. Lots of good podcasters choose to use AI to learn, which helps them ask better questions of experts on their podcasts, which helps me to learn about a much wider range of topics than I did pre-ChatGPT.

In turn, I use AI to extend my Zone of Proximal Development. When I’m listening to an AI researcher on Latent Space, or a biochemist on Mindscape and a technical detail goes over my head, I sometimes choose to pause the podcast, switch to the Claude app, provide a link to the transcript (which the podcaster generated with AI), ask for an explanation (using voice input which is much faster than typing), follow-up if needed, then switch back to the podcast. I do all of this from my phone, while walking around campus.

These exemplify the flywheel effects that are enabled by choosing to use AI to learn. They also seem quaint, relative to some AI Native workflows.

The point is: AI amplifies intent.

Choose to input lazy prompts which avoid thinking? Produce slop. Choose to write demanding prompts affording learning? Build a nuclear fusor.

None of this is to say that humanities faculty need to match HudZah’s technical sophistication. This Fall, we have to teach what we’ve always taught: how to reason critically, how to question comfortable assumptions, how to sit with ambiguity, how to entertain opposing views charitably. One difference between my “Digital Wisdom” course and those with AI “bans” is the recognition that these skills apply at least as much, maybe more, to prompting AI as they do to reading texts or writing essays.

You can subscribe to posts by Dr. Skorburg about his course here.

* Beyond learning-focused applications, custom instructions are generally quite useful for getting LLMs to stop doing things you find annoying or unhelpful. I get a lot of mileage out of custom instructions like: “write at the level of a tenured academic”; “Avoid flowery, overly cheery language, sycophantic responses, or engagement-driven questions”; “Never use phrases like ‘fascinating,’ ‘great point,’ or ask follow-up questions for engagement”; “Provide comprehensive, detailed explanations without concern for length”; “Prioritize critical, analytical thinking over tone-matching or making the user feel good”; “avoid unnecessary juxtapositions of the form, “Its not X, its Y”.

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AI Research

Proactive, Autonomous, Seamless Customer Support

Published

49 minutes ago

September 16, 2025

Stefan Steinle

SAP Business AI can boost productivity with technology that aligns with the AI strategies of our customers—ranging from building effective agents to managing intelligent systems.

Among the many announcements at SAP Sapphire in 2025, the company unveiled new innovations, partnerships, and integrations that can deliver real-time, proactive assistance. For example, SAP’s AI copilot Joule is now available to users across SAP and non-SAP systems. SAP also expanded its agentic AI footprint across SAP Business Suite by introducing Joule Agents for multiple use cases and an evolving AI Foundation as the AI operating system designed to simplify development, enabling developers to build, deploy, and scale solutions with ease.

The impact of AI on the delivery of customer support at SAP

As announced in Q2 this year, SAP’s simplified, tiered, services-and-support engagement model will be generally available in early 2026. Here, SAP’s customer support is a centerpiece of the Foundational Success Plan, delivered via the proven SAP Enterprise Support offering included in every SAP cloud solution subscription. The Foundational Success Plan can support in-house teams by helping to onboard and run solutions, keep business continuity, and drive ongoing value. It includes customer self-service options, application lifecycle management solutions centered around SAP Cloud ALM, and preventative mission-critical support. With the plan, SAP turns on Joule for a customer’s business and supports the team ramp-up with learning journeys for SAP Business AI.

When it comes to customer support in general, agentic AI can redefine the support process by moving beyond scripted responses and basic automation. It can assess situations, make decisions, and take action—often before the customer even knows there’s an issue. SAP’s customer support harnesses agentic AI to help deliver smarter assistance, faster resolutions, and a stronger human–tech partnership.

We focus on elevating support experiences for customers and improving support delivery for engineers by employing a combination of agents and assistants. For example, we use autoresponders and smart log analyzers to help process issues, while configuration advisors, language services, and proactive notifiers can guide customers toward self-service solutions. At the same time, our support engineers rely on co-pilots to help summarize cases, recommend solutions, escalate using intelligence, assist with communications, and create a continuous feedback loop for learning. For strategic customer support, we use tools like feedback collectors to help capture customer insights and channel recommenders to help ensure that every interaction is handled in the right channel. Together, these innovations can redefine support as faster, smarter, and more human.

The impact for customers

When it comes to SAP Business AI, we build trust and create customer confidence by being relevant, reliable, and responsible. Unlike traditional AI that only suggests answers, agentic AI can reason, decide, and take action. For customers to feel confident, they expect accuracy, reliability, and transparency from the system.

As we support and guide our customers, we recognize that while agentic AI is a game-changer, it is not a magic pill. Coupled with ethical and responsible AI, real impact comes from SAP’s business expertise and a deep understanding of what our customers truly need. When knowledge is combined with AI to infuse autonomy and interoperability in our agents, we can unlock the ability to simplify processes, remove friction, and deliver experiences that feel effortless.

AI technology amplifies human insight and delivers delightful user experiences, but when it comes to business AI, it is our domain expertise that fuels SAP Business AI into a tool for creating genuinely easy, productive, and meaningful experiences for our customers.

Stefan Steinle is executive vice president and head of Customer Support & Cloud Lifecycle Management at SAP.

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AI Research

How AI-powered ZTNA will protect the hybrid future

Published

52 minutes ago

September 16, 2025

Raghav Potluri

What I’m seeing in zero-trust deployments

The real story isn’t in the survey data — it’s in the conversations I’m having with enterprise security architects trying to implement zero trust strategies. Last month, I worked with a financial services company that had spent eighteen months evaluating ZTNA solutions. They’d built requirements documents, conducted vendor demos and mapped their application inventory. But when it came time to deploy, they hit a wall.

The problem wasn’t technology. Gartner’s Magic Quadrant shows vendors like Palo Alto Networks, Netskope and Zscaler have mature platforms. The problem was that implementing these solutions required untangling years of VPN configurations, documenting legacy application dependencies and coordinating with stretched application teams.

What struck me was hearing their CISO say, “We bought this ZTNA platform for intelligent, automated access control. Instead, we’re spending more time on manual policy creation than with our old VPN.” That’s when I realized we’re dealing with a deeper issue than technology selection.

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AI Research

The impact of artificial intelligence on the food industry

Published

1 hour ago

September 16, 2025

The Editors

The integration of artificial intelligence (AI) into the food industry is revolutionizing the way food is produced, processed, distributed, and consumed. AI-driven solutions offer unprecedented opportunities for improving efficiency, ensuring safety, reducing waste, and enhancing sustainability in this vital sector. This article explores how AI is transforming various facets of the food industry, from farm to table.

AI in agriculture

The food production process begins on the farm, where AI technologies are helping farmers make smarter decisions. Precision agriculture, powered by AI, uses data from sensors, drones, and satellites to monitor crop health, soil conditions, and weather patterns. Machine learning algorithms analyze this data to provide actionable insights, such as when to irrigate, fertilize, or harvest crops. This approach not only boosts yield but also minimizes the use of water, fertilizers, and pesticides, reducing environmental impact.

Robotics is another AI application making waves in agriculture. Autonomous tractors and robotic harvesters equipped with AI can perform labor-intensive tasks with precision, addressing labor shortages and reducing costs. For instance, AI-enabled robots can differentiate between ripe and unripe fruits, ensuring only the best produce is picked.

Enhancing food processing and manufacturing

AI is playing a critical role in food processing and manufacturing by optimizing operations and ensuring quality control. Advanced vision systems powered by AI can inspect food products for defects, contaminants, or inconsistencies at a speed and accuracy unmatched by human workers. This ensures that only safe and high-quality products reach consumers.

Predictive maintenance is another area where AI is proving invaluable. By monitoring machinery and analyzing operational data, AI can predict equipment failures before they occur, minimizing downtime and maintenance costs. This level of foresight is especially important in food manufacturing, where delays can lead to spoilage and significant financial losses.

In addition to improving efficiency, AI-driven automation is enhancing worker safety by taking over hazardous tasks, such as handling hot or sharp equipment. This contributes to creating a safer work environment in food processing plants.

Supply chain optimization

The food supply chain is a complex network that requires precise coordination to ensure timely delivery of perishable goods. AI-powered tools are streamlining supply chain management by improving forecasting, inventory management, and logistics.

Demand forecasting is a key application of AI in this domain. By analyzing historical sales data, market trends, and external factors like weather or holidays, AI systems can accurately predict demand for different food products. This helps retailers and suppliers avoid overstocking or understocking, reducing food waste and increasing profitability.

AI is also revolutionizing logistics through route optimization and real-time tracking. Advanced algorithms can determine the most efficient delivery routes, reducing fuel consumption and ensuring products reach their destinations as quickly as possible. Additionally, AI can monitor the condition of perishable goods during transit, ensuring they remain within safe temperature ranges.

Enhancing food safety and quality

Food safety is a top priority in the industry, and AI is proving to be a powerful ally in this area. Machine learning algorithms can analyze vast amounts of data from production lines, environmental monitoring systems, and lab tests to identify potential risks or contamination sources.

AI-powered tools are also aiding in the rapid detection of pathogens like Salmonella and E. coli. Traditional testing methods can take days, but AI-based systems can deliver results in hours, enabling quicker responses to potential outbreaks. Moreover, blockchain technology combined with AI is enhancing traceability, allowing stakeholders to track the journey of a product from farm to fork. This transparency helps build consumer trust and simplifies recalls in case of contamination.

Reducing food waste

Food waste is a significant global issue, and AI is offering innovative solutions to address this challenge. AI systems can analyze data from supermarkets, restaurants, and households to identify patterns and suggest ways to reduce waste. For instance, AI can recommend optimal stock levels for retailers, ensuring they do not overorder perishable items.

In the hospitality sector, AI-powered tools can monitor inventory and predict demand, helping chefs prepare just the right amount of food. This not only reduces waste but also cuts costs. Additionally, AI is being used to repurpose surplus food by identifying ways to incorporate it into new recipes or distribute it to those in need.

Personalized nutrition and consumer experience

AI is transforming the way consumers interact with food, offering personalized recommendations based on individual preferences, dietary restrictions, and health goals. Apps and wearable devices equipped with AI can analyze user data to suggest meal plans, track nutritional intake, and even offer cooking tips.

Retailers are also using AI to enhance the shopping experience. AI-powered chatbots and virtual assistants can guide customers in selecting products, answer queries, and provide tailored suggestions. Meanwhile, AI-driven shelf management systems ensure that popular items are always in stock, improving customer satisfaction.

Driving sustainability

Sustainability is a pressing concern for the food industry, and AI is helping companies adopt greener practices. By optimizing resource usage, reducing waste, and improving supply chain efficiency, AI is enabling the industry to lower its carbon footprint.

AI is also playing a role in developing alternative proteins, such as plant-based or lab-grown meat. Machine learning models are being used to optimize formulations, improve texture and taste, and scale production. These innovations are contributing to a more sustainable and ethical food system.

Challenges and future prospects

While the benefits of AI in the food industry are immense, challenges remain. High implementation costs, lack of technical expertise, and concerns about data privacy are some of the barriers to widespread adoption. Additionally, there is a need for robust regulations to ensure ethical use of AI and address potential biases in decision-making.

Despite these challenges, the future of AI in the food industry looks promising. As technology continues to evolve, we can expect even more sophisticated applications that further enhance efficiency, sustainability, and consumer satisfaction. Companies that embrace AI today will be well-positioned to lead the industry into a smarter, more sustainable future.

In conclusion, AI is not just a tool but a transformative force reshaping the food industry. By harnessing its potential, stakeholders can address some of the most pressing challenges in food production, safety, and sustainability, ultimately creating a better food system for everyone.

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