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If this episode makes you think, please let us know in the comments and support us by subscribing and leaving a review. Thank you. Today we are exploring a really insightful piece from the conversation that urges higher education to stop policing AI in the classroom and instead start teaching it. It makes a compelling case for why universities, particularly drawing on examples from Malaysia and and Indonesia, need to shift from resistance to instruction. Especially when you consider that more than one third of entry level jobs now require AI skills, which is nearly triple the share we saw back in the fall of 2025. That's a staggering rate of change. The article really opens with a point that I think resonates with so many of us in education, drawing a parallel between the current panic and around generative AI and past fears. Think back to the 1970s, when handheld calculators first arrived in classrooms. Critics sounded the alarm, worried about a wave of math illiteracy. Then, with the Internet, the fear shifted to the death of reading. But what happened? Neither apocalypse actually materialized. Instead, education adapted, right? We turned those disruptors into essential extensions of human intelligence. And that's precisely the perspective we need for AI in higher education, and frankly, in all levels of education. It's an evolution, not a revolution. And we've seen this before. The piece makes a really strong case that generative AI marks a similar turning point. The authors point out that in lecture halls across Malaysia and Indonesia, students are routinely using tools like ChatGPT, Gemini, and Claude for everything you can imagine, from research and writing to coding and problem solving. And let's be honest, this isn't just happening in Southeast Asia, it's happening everywhere. Yet many universities, the article notes, are still pushing back. They're introducing stricter regulations, academic penalties, and even relying on AI detection software. My view on AI detection tools is pretty clear. They're unreliable, often harmful, and to be honest, they represent a kind of institutional denial. They shift the focus from teaching and learning to policing, and that fundamentally misjudges the challenge we're facing. The first thing this article really hammers home is that we need to stop thinking of AI as a problem to police. The question isn't whether students should use AI, but they already do. The real question, as the article rightly puts it, is how we will train them to use it responsibly. Instead of fighting generative AI as a threat to academic integrity, institutions should embrace it as a vital productivity tool. And the piece suggests focusing on three key teaching AI literacy, redesigning assessments, and embedding ethical frameworks. So the second crucial point is about seeing AI as a core productivity skill. Future graduates are going to enter workplaces where AI is assisting with everything from predictive analytics to legal research and strategic decision making. That statistic I mentioned earlier, more than one third of entry level jobs now require an AI skills. It's not just a number, it's a call to action. It shows that employers are actively seeking early career talent who can use AI effectively in their work. This is why universities really should be teaching AI as a professional competency, not just a niche technical skill. What does that mean in practice? Well, students need to learn how to construct effective prompts. They need to know how to evaluate machine generated outputs, to detect those infamous hallucinations, to verify evidence, and crucially to recognize the limitations of AI systems. These aren't optional digital skills anymore, they're essential competencies for an AI enabled workforce. This approach, as the article highlights, is completely consistent with UNESCO's call for a human centered model of AI governance that prioritizes AI literacy over blanket prohibition. We're not talking about simply memorizing tool features, we're talking about developing a collaborative reasoning ability to knowing how to think with AI, not just use tools. The piece offers some inspiring examples suggesting that Malaysia and Indonesia have a real opportunity to lead this transformation. Indonesia's recent Joint Ministerial decree signed by seven ministers is a significant step in regulating AI across education. While it places greater restrictions on the use of instant answer AI IT in primary and secondary schools, which makes sense for foundational learning, it also really highlights the need for universities to develop clearer operational frameworks for responsible AI adoption. And Malaysia, with its ambitions to become a regional digital and knowledge economy, should similarly position higher education as a driver of AI literacy. Rather than promoting AI avoidance, universities should be encouraging students to move beyond using AI merely to generate assignments. We want them to become creators of AI driven solutions. Think about a specific example the article gives. In Malaysia, Islamic economics and finance, students could develop specialized AI applications trained on key concepts like Maqassid al Sharia, which is a framework for human well being and justice, or waqf, which are charitable endowments, zakat and Sharia compliant investment principles. Imagine applying that principle in your own context. A year eight geography class could use AI to analyze climate data specific to their local area, identifying patterns and proposing solutions rather than just using it to summarize a report. It's about empowering students to outsource their doing, not their thinking, so they can focus on the higher order, creative and problem solving work. AI should become a platform for innovation, not just a shortcut and that leads us directly to the third and perhaps most challenging area, redesigning our assessments. When assignments merely reward memorization, generative AI is always going to feel like a threat. The real challenge isn't the technology itself, it's how we design what and how we assess. The article advocates for universities to shift away from traditional take home essays towards assessments that truly measure reasoning, judgment and application. This means a greater emphasis on things like oral presentations, live case analyses, project demonstrations, reflective portfolios, and collaborative innovation challenges. This aligns so perfectly with my three P's for assessment. Looking at the product, yes, but also the process. How did they get there, including their AI interaction logs and their performance. A live demonstration of understanding When AI is used, students should absolutely be assessed on how they engaged with it. What prompts did they design? How did they identify inaccuracies or misinterpretation? Which sources did they use to verify AI generated information? How did AI contribute to rather than replace their own analysis? This is the core of it, isn't it? The real value is not in what the machine produces, but in how the student responds. By assessing that learning process instead of simply the final written product, universities can preserve academic integrity while encouraging responsible technological adoption. It helps us design learning that cannot be faked because it demands depth, care and imagination. It's all about providing that cognitive stretch. If you're finding these insights valuable and want more discussions on how AI is reshaping education, make sure to follow and subscribe to the podcast wherever you listen. Finally, the article rightly emphasizes embedding ethical frameworks. Teaching AI without ethics leaves graduates completely unprepared for the real world responsibilities that come with these powerful technologies. This is why universities, and indeed all schools, need to twin AI literacy with ethical literacy for Malaysia. The article suggests this dialogue can be grounded in the principles of Maqassid al Sharia, which holds that any policy or practice must be evaluated by its ability to protect five core human faith, life, intellect, lineage, and wealth. This is such a powerful example of ground in ethics in a specific, culturally relevant framework. We can apply this principle in any school setting by asking, what are our core values? How do we evaluate AI tools and practices against those values? AI tools that genuinely drive financial inclusion, alleviate poverty, or simplify complex policy. They preserve intellect and wealth. But using AI to fake research or to deceive teachers or to bypass critical thinking, those actions directly violate principles like intellectual honesty and critical thought that we hold dear. Ethical AI education should not just be limited to preventing misconduct. It should cultivate graduates who truly understand transparency accountability, intellectual honesty and the social responsibilities that accompany technological innovation. This is about ensuring humans remain the decision makers with AI as a tool and acknowledging that humans are always accountable for AI assisted decisions. Ultimately, universities should not aspire to produce graduates who simply know how to use AI. They should produce graduates who know when to trust it, when to question it and when human judgment must absolutely prevail. This entire approach to AI in higher education for focusing on AI literacy for students, redesigning assessments for the AI era and embedding ethical AI education is what will truly prepare graduates for an economy where AI is an everyday professional tool. The universities that lead the next generation won't be the ones that police AI the hardest. They'll be the ones that empower students to use it more intelligently, creatively and ethically. Ultimately, it's about evolving our thinking so we can empower students to outthink the machines rather than just outsmart them. That's all for today. Thanks for listening.