It would be easy to dig in our heels and reaffirm the status quo, but others may soon become sceptical. Personally, I think it’s worth reflecting on how universities can best add value in an age where information is free.

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Six months ago, I was actually optimistic about designing assessments that could outsmart Claude. But now, after teaching 170 students and learning from wider research, I think we need to fundamentally redesign teaching in an age of AI.

This is not a manifesto, but an open reflection to share with colleagues grappling with the same quandaries. Ideas and feedback are welcome!

"AI-Proof" Assessments?

In August 2024, I was concerned by the new risk plagiarism, yet eager to experiment. Over the past semester, I piloted a two-pronged approach:

1. Embrace AI openly in teaching to develop technological literacy

2. Design assessments that would go beyond AI’s capabilities

I crafted questions requiring deep understanding of course material and creative thinking, such as:

• How do Hindu Nationalism and Indonesian Islamic movements counter Acemoglu and Johnson’s arguments in “The Narrow Corridor”

• Design interventions for South Korea’s low fertility, providing academic justification

• How do environmental histories challenge conventional wisdom about Middle Eastern authoritarianism?

Initial tests against Claude suggested these questions were beyond AI’s capabilities…

Embracing AI in the Classroom

Eager to prepare my students a workplace that increasingly rewards tech skills, I taught them how to use Generative AI. In every single lesson, students worked in pairs to pose questions to Claude, evaluate its responses, and improve. We used it to:

• Analyse land reform's role in East Asia's industrialisation

• Interpret regression tables from key papers

• Explore how Communism mediates the relationship between industrialisation and democratisation

As outlined in “From Hesitation to Mastery”, my goal was to create a level playing field where all students would be able to use AI effectively and critically. By highlighting its benefits, I hoped to encourage universal adoption, enabling me to to set demanding assessments.

Confronting Reality

Now, having marked 170 undergraduate and postgraduate essays, I realise two major hurdles:

1. Tech-Hesitation

Even though I integrated AI into teaching, not all students feel equally comfortable. Recent research finds that women are 25% less likely to use generative AI globally. This gap persists across regions, sectors, and occupations - as discussed in my earlier essay, “Are Women Missing Out on AI?”.

Prohibiting AI proves counter-productive for several reasons:

• Detection is difficult

• Students may be disadvantaged if they do not use the latest technologies

• Unenforceable bans exacerbate inequalities

At one Norwegian business school, when AI was permitted, usage intentions were similar between genders (87% men, 83% women). But with a ban, men’s intended usage dropped modestly to 67% while women’s plummeted to 46%. High-achieving women were especially rule-abiding.

This creates a fundamental challenge:

• How can we accurately identify talent in anonymous assessments when we cannot determine who used AI?

• Should we penalise students who do not harness the most advanced technology, or

• Should we reward human-only essays that demonstrate genuine understanding?

2. AI’s Rapid Evolution

While my earlier questions could stump Claude, its capabilities have since advanced. Students needn’t rely on its training data. If you copy and paste my textbook on international development (which includes a breadth of theories, evidence, and references), then pose my assessment questions, the answers are excellent!

Hoisted with my own petard!

As AI rapidly improves, with better accuracy, fewer hallucinations, and broader knowledge, even our most challenging essay questions become easy for machines.

Quandaries!

We face an impossible task in fairly evaluating student work. When AI use is both undetectable and offers significant advantages, marking becomes difficult. OK. Here’s a serious question:

Yet remember, we can’t distinguish whether they used ChatGPT! So it’s like judging a race without knowing whether someone has sprinted or driven a car!

The Learning Crisis

This rapid advancement raises a deeper concern. If AI can consistently produce undetectable, persuasive essays, what motivates deep learning? Why should students spend hours wrestling with the knottiest problems and complex ideas when AI can craft compelling analysis? Why bother when machines achieve the same result?

We risk producing graduates who can submit excellent work while understanding very little. This threatens not just academic integrity but career readiness and productivity!

Sceptics might advocate returning to closed book, in-person assessments. Now, perhaps these are useful as a diverse package of assessments, but other exams should also encourage our students to harness the technological frontier. This creates a puzzle: how can we encourage both deep learning and technological mastery?

A Path Forward: Two-Stage Assessment

After considerable consideration, I propose combining essays with oral examinations:

1. Written coursework, with AI use explicitly permitted

2. A follow-up 10-minute video interview where students explain their work

This structure promotes both AI literacy and authenticity:

• Students gain technological confidence through essay preparation

• Oral components verify genuine understanding

Yes, interviewing 170 students requires approximately four working days. However, before professors all unsubscribe - this investment may be vital to our security, as it ensures we can actually motivate and reward talent!

As the the world becomes increasingly trapped in the easy conveniences and entertainment of home-bound isolation, schools and universities could actually be a bit subversive: nurturing what makes us distinctly human - our ability to connect, communicate, and persuade through personal interaction.

Looking Forward

Assessment methods shape the entire university experience, influencing how students engage with material, manage time, and develop skills. We must ensure our assessments target what truly matters: the ability to leverage technology while developing the deep understanding necessary to explain and apply knowledge effectively.

While traditional take-home exams have become untenable, combining written work with oral assessment encourages both technological mastery and deep learning. University departments might also consider stronger integration of Generative AI, helping all students become tech-savvy. This combined approach acknowledges AI's utility while ensuring students develop the fundamental understanding and communication skills that make us human.

What do you think?

Related Essays

• Crafting AI-Complementary Skills and Bulletproof Assessments

• Are Women Missing Out on AI?

• From Hesitation to Mastery: Integrating AI in University Teaching

Still Sceptical?

Today, I asked Claude: “why might the relationship between industrialisation and democratisation be different in communist societies? please write a 1500 word essay, with references.

So how would you mark this?

If students can easily generate sophisticated essays - complete with nuanced arguments and appropriate references - what motivates learning? Why deeply engage with readings when AI can craft compelling analysis? Why grapple with complex ideas when machines can explain academic debates?

We risk creating a generation of graduates who can submit excellent work while understanding very little. Ill-conceived assessments could jeopardise not just student learning, but their future careers.

We should act. But how?

Ban Tech?

We could revert to closed-book in-person assessments, writing on paper. Alternatively, we might allow students to use computers, without the internet.

Sure, that would prevent plagiarism.

But at what cost? Might it discourage students from exploring Artificial Intelligence? I want my students to be at the technological frontier, carefully and critically marshalling the latest innovations. This is hugely important for gender equality - existing studies suggest women are 25% less likely to use Generative AI.

But I also want to ensure they become deep thinkers. This presents a conundrum. How can we encourage both deep learning and technological mastery?

Two-Stage Assessment: Essays & Interviews

The mode of assessment shapes the entire university experience. It signals what matters, influencing how students engage with their readings, structure their time, and develop key skills. Given this profound impact, we must ensure our assessments directly target deep learning.

For future years, I propose a combined essay and oral assessment. Students have 3 hours to write 2 out of 6 essay questions, followed by a video interview to explain their work. Not complex. Just a straightforward 10-minute conversation where they explain their argument, discuss their key references, and demonstrate their understanding.

This two-stage assessment process motivates precisely what employers want: the capacity to harness and direct technology while developing the in-depth understanding needed to explain those findings. More fundamentally, it rewards what makes us distinctly human - our ability to connect, communicate, and convince others through personal interaction.

Need to explain their work motivates students to forge crucial social skills. Clear and persuasive communication emerges naturally, as does mastery of complex ideas and quick thinking. Such competencies complement AI, prioritising interpersonal abilities.

For my two core modules, this means interviewing 170 students. Each conversation takes 10 minutes, with additional time needed for scheduling and administration. That is about four working days.

Fostering Deep Learning in an AI Age

As AI races forward, I want to be proactive, ensuring my assessments motivate students to become tech-savvy, deep thinkers and creative learners.

Your feedback and critique would be superbly useful.

Collectively, how might we refine this approach?

Related Essays

• Crafting AI-Complementary Skills and Bulletproof Assessments

• Are Women Missing Out on AI?

To level the playing field, I directly incorporate Claude into my lectures. Ultimately, my goal is to demonstrate its value, build confidence, and signal permissibility.

Now let me share an example from today’s classes - respectively with undergraduates and postgraduates.

Brainstorming with AI

Today’s lecture focused on why some countries have democratised while others remain authoritarian. We explored various theories, including Sam Van Noort’s research on industrialisation and democracy. His key finding: the share of manufacturing employment is positively correlated with democratisation.

Students read this text alongside Hagen Koo’s fantastic book on the Korean workers’ movement.

Slides below

Interactive Learning with AI

Now, I want to push my students to consider how the political effects of industrialisation might be mediated by Communism.

I structured the exercise in several steps:

1. Paired Brainstorming: Students worked in pairs to draft essay plans on: “How might Communism affect the relationship between industrialisation and democratisation?”

2. Plenary Discussion: We explored their ideas as a class, with me providing feedback and further analysis.

3. AI Integration: I demonstrated how to pose these same questions to Claude, showing students how to:

   • Request specific feedback

   • Iterate on their ideas

   • Compare AI responses with their own analysis

Claude not only echoed points we had already discussed, but also added further examples and apt references. Students are then encouraged to read the recommended books!

Expanding the Discussion

Returning to my lecture slides, I shifted to contemporary challenges:

• Premature deindustrialisation

• Technological surveillance

After my lecture presentation, I showed my students how to ask these same questions to Claude.

“We Want to Be Masters of Technology”

By incorporating AI directly into my lectures, we can help our students:

• Learn how to use AI effectively

• Craft strong prompting techniques

• Gain confidence through hands-on experience

• Value AI as entirely legitimate.

This integration serves several purposes. In the short term, it’s a fun form of active learning: asking questions and getting immediate feedback. Students also prepare for assessments, where they have full freedom to use AI. More importantly, they develop crucial skills for the modern workplace, where AI literacy is increasingly valued.

Frankly, I want all my students to be at the technological frontier - whether they are strengthening ‘digital democracy’ or pursuing careers in Law. If Britain wants to ramp up productivity or pioneer AI safety, we need to be technologically savvy. Universities - as centres of learning - can serve as powerful catalysts, enabling students to leverage AI to push the boundaries of innovation.

Finally, because learning is fundamentally a collective endeavour, sharing and learning from each other, we all went out for dinner. And took over an entire restaurant! 😀

Related Essays

• Are Women Missing Out on AI?

• Crafting AI-Complementary Skills and Bulletproof Assessments

“[AI] presents an enormous opportunity for our practice and for all law firms . . . and enormous risks. It’s moving at such an incredible pace that if you don’t start you can get left behind.”

Adoption of Generative AI has been seriously rapid - faster than historical patterns of using personal computers and the internet. It’s all happening very fast.

Yet, women worldwide are 24.6% less likely to use these new powerful technologies. Well-intentioned efforts to prevent plagiarism may amplify these inequalities. A striking study from Norway finds that banning AI creates massive gender gaps in usage, which could jeopardise future earnings.

In this essay, I unpack three new studies on gender differences in adoption of Generative AI, then discuss how academics might provide more inclusive education.

Global Meta-Analysis

Otis and colleagues combine extensive survey analysis, web traffic data, and field experiments. They synthesise data from 16 independent studies surveying over 100,000 individuals across 26 countries. Harnessing Google Scholar, they incorporate all studies that measured both AI use and respondent gender. Samples ranged from U.S. working professionals and Danish workers to global science postdocs and Kenyan entrepreneurs.

To validate these survey findings with behavioural data, Otis and colleagues analysed web traffic to major AI platforms using Similarweb data. Their analysis covered traffic to prominent platforms from November 2022 through May 2024. This includes 173 million monthly unique visitors to ChatGPT alone, along with 10 million to Midjourney, 7.1 million to Perplexity, and 1.9 million to Anthropic.

To ascertain whether gender gaps might be a function of access, Otis and colleagues ran two field experiments in Kenya:

• 640 small business entrepreneurs were given access to an AI-powered WhatsApp business mentor.

• 17,541 Facebook users were offered the opportunity to try ChatGPT.

Women were 24.6% less likely to use AI

This gap persisted across nearly all regions, sectors, and occupations, with only one notable exception: a BCG study of U.S. tech workers showed 3% higher adoption among women, driven primarily by senior women in technical roles.

This pattern was corroborated by web traffic analysis, across low- and high income countries. Women comprised only 42% of ChatGPT users globally, with similar patterns appearing across other platforms: 42.4% of Perplexity users, 39.6% of Midjourney users, and just 31.2% of Anthropic users.

Even when field experiments equalised access, there was a persisted gender gap. Women were 14% less likely to seek AI advice in the WhatsApp experiment, and when offered the opportunity on Facebook women were 12% less likely to use ChatGPT.

What’s the Wider Evidence ?

A comprehensive survey of 100,000 workers in Denmark finds that women are less likely to use ChatGPT even within the same occupations - suggesting the gap isn’t just about industry segregation.

Carvajal, Franco, and Isaksson’s findings from a top Norwegian business school are striking. Female students are 25% less likely to report high use of Generative AI, with high-achieving women being particularly reluctant.

This isn’t just about ability - these women express more skepticism about AI and report less confidence in their prompting skills.

Female students are also less confident in their own prompts, and these attitudes are correlated with success. Self-efficacy predicts persistence, practice and learning.

Bans on AI may compound inequalities

Here’s the most important discovery: banning AI may create a massive gender gap. When AI is permitted, usage intentions are similar - 87% for men and 83% for women. But introduce a ban, and men's drops only modestly to 67% while women's intended usage plummets to 46%.

High-achieving women are especially rule-abiding.

The stakes are high. In a survey of employers, female candidates with AI skills were rated 7.6% higher, with managers overwhelmingly indicating they would promote workers who boost productivity using AI.

When academic policies discourage adoption of the latest technologies, they may inadvertently push women away from skills that increasingly drive workplace success.

Gender differences in college majors

These studies on Generative AI are consistent with the wider literature on the gender pay gap. Today, choice of college major is a major predictor of subsequent earnings. Yet women are less likely to major in STEM fields that ultimately pay more (accounting for 63% of entry-level pay gaps among US college graduates and 51% in Italy). We’re seeing similar self-selection away from productivity-enhancing AI.

How Might Universities Respond?

Vibes shape reality, particularly in universities as incubators of both culture and innovation. If professors portray technology as a threat or taboo, students may internalise that disdain, stunting their AI literacy and proclivity to explore.

Actually, there is a deep irony - academic communities committed to diversity risk reproducing gender inequalities if they demonise AI tools that could help level the playing field.

While broader questions about tech regulation loom, my role as a lecturer is to nurture inclusive education that helps students harness the technological frontier.

I actively demonstrate AI's benefits across the entire student cohort, focusing on building confidence, competence, and technological curiosity. During lectures, we engage with AI tools like Claude in real-time, collaboratively exploring student questions and analysing responses. Students learn to view AI as a writing partner - receiving feedback on drafts and mastering effective prompting techniques.

These hands-on exercises serve dual purposes: normalising AI while sharpening critical thinking. By explicitly welcoming use of AI in assessments, the classroom atmosphere mirrors modern workplace realities, where AI isn’t just permitted but increasingly essential.

My hope? That every student (including women) leave my classroom feeling confident about harnessing these powerful tools, ready to creatively innovate and explore!

Related Essays

• Crafting AI-Complementary Skills and Bulletproof Assessments

Our brains aren’t mere buckets waiting to be filled with knowledge. They’re more like playgrounds, thriving with interaction and engagement. When we actively grapple with new ideas - comparing, questioning, or applying them - we’re creating connections, joining the dots, strengthening our lateral thinking.

University is a really special opportunity to build a vibrant learning community. Collaboration massively enriches education, far more so than any tech or quiet study. Meeting people from different backgrounds, engaging in discussions, and learning together creates a dynamic atmosphere that fosters creativity and critical thinking. Community matters, as strengthened by collective rituals. That’s how we bond as a group, build trust and feel in sync. At King’s College London, we are so fortunate to welcome brilliant minds from around the globe, sharing diverse perspectives and forging friendships.

But now we have a serious challenge..

The Internet is Very Distracting!

Millions of dollars have been poured into making online apps exciting and engaging, diverting user attention with a tsunami of treats to keep them tantalised. The constant stream of emails, social media alerts, messages, and new photos generates perpetual distraction.

Even if devices are on silent, they still dispense little hits of dopamine, engineered by tech wizards to keep users hooked. Let’s be honest, I’m just a little academic with niche interests in culture and structural transformation. For all my ‘jokes’ and ‘fascinating insights’, I simply cannot compete with internet sirens.

How Should We Respond?

Across the United Kingdom and Australia, teachers are now prohibiting phone use. Most British parents are supportive.

Universities, meanwhile, are still laptop-friendly. What’s driving this asymmetry? What’s the difference between scrolling on your phone or your laptop? Whether an email pings in your palm or your lap, it’s equally distracting.

Libertarians may object that university students are adults, who can make their own informed decisions. And that’s true. But by choosing to come to my class, students are implicitly recognising my greater knowledge.

14 years of teaching has given me valuable insights into how to create conducive learning environments. Supporting students, listening to their feedback, I’ve learned how to build community and foster connections within my teaching. This empirical evidence of what works gives me some authority to structure our style of learning.

Laptops create negative externalities

Susan Dynarski (Harvard Professor of Education) is very clear: ‘students learn less when they use computers during lectures’. Careful empirical research also shows negative externalities. Students perform worse when others nearby are on laptops!

If half my students are lost in a Whatsapp wormhole, this depresses participation. Seldom seeing others ask questions, others may be nervous to raise their hand. No one wants to be the odd one out. Frankly, the entire class becomes dull! If I ask students to discuss trade liberalisation in pairs, but half have been distracted by messaging apps, then conversations may hit a brick wall. Phones suck attention, weaken engagement, and basically decimate expectations of fun, enjoyable, active participatory discussions. In the long run, this will affect attendance. Why bother coming if no one talks?

Addressing concerns

Now let me consider another objection. “What if students want to take notes?”. Actually, that’s great! Anyone bursting with big ideas, questions and contributions is always welcome to use pen and paper. My goal isn’t to suppress note-taking, but to encourage present, engaged and collective participation.

Professor Dynarski adds another exception, for those with learning disabilities. I agree. This is an important part of inclusivity.

And I’m no Luddite! At times, I will explicitly encourage my students to engage with Research AI tools (like Claude), such as to get instant essay feedback. However, in order to create AI-complementary skills, we also need to build in-person connections. That’s how we capitalised on our uniquely human edge and outsmart AI.

Creating a Connected Community

Ultimately, I want to build a vibrant learning classroom experience. Phones and laptops can be useful, but they’re also like that chatty friend at the cinema, distracting from the main event.

So, to play on my earlier metaphor, here’s where I’ll actually limit competition and be a bit protectionist. By asking students to stow their devices, I’m cultivating an environment where people truly connect. This approach encourages my students to tune into each other, facilitating collaborative learning and community.

Further Resources

• Susan Dynarski, 2017 “For better learning in college lectures, lay down the laptop and pick up a pen”

• Ken Bain, 2021 Super Courses: The Future of Teaching and Learning

Related Essays

• Crafting AI-Complementary Skills and Bulletproof Assessments

The Perils of Business as Usual

Before we dive into solutions, let’s consider the consequences of inaction. If universities merely ask students to promise no plagiarism, and otherwise continue with business as usual, we create four significant risks:

1. Falling behind: Some students may disregard AI altogether, leaving them ill-equipped for a labour market that increasingly rewards technological mastery. A new survey of 100,00 workers in Denmark finds that, even within occupations, women are less likely to use ChatGPT. This puts them at a disadvantage.

   

2. Skills development: Many students will use AI tools (since they provide decent answers, yet cannot be checked or punished), but not learn how to use these technologies critically. Moreover, if our assessments stay the same, we are not encouraging students to creatively harness the technological frontier.

3. Repressive culture: My globally comparative research, as well as a broader empirical literature, indicates the crucial importance of a culture where everyone is encouraged to openly question, inspire, critique, and explore. As Timur Kuran articulates in his seminal work “Private Truths and Public Lies”, a culture of repression comes with serious, often hidden costs. By prohibiting the use of AI, universities risk recreating climates of fear and secrecy. Students may not share new strategies or insights gained from AI tools, for fear of getting in trouble. This hurts learning, especially for marginalised groups.

   

   

4. Credibility: Firms heavily rely on university credentials as signals of talent. Universities must maintain public trust, by demonstrating our capacity to identify and certify talent. That’s the point of a degree. We need to show that our assessments cannot be outsmarted by ChatGPT.

These risks underscore the urgent need for adaptation and leadership. But how can we rise to this challenge? I suggest we embrace technological progress while innovating to build AI-complementary skills and ensuring robust assessments.

Harnessing AI: A Path Forward

Technology is a wonderful thing - for both global and individual prosperity.

As a lecturer in International Development, the overarching lesson from our field is that technological innovation is absolutely fundamental to:

• Agricultural productivity (high-yielding seed varieties)

• Economic upgrading (hello Samsung!)

• Sustained economic growth

• Environmental sustainability (Electric Vehicles and Solar Panels)

• Global health (e.g., the latest malaria vaccines).

Universities should be at the technological frontier: encouraging the pursuit of knowledge, freely sharing new insights, and driving progress.

So, instead of asking, “How can we prevent students from using AI?”, we could be thinking more constructively:

“How can we enable our students to harness advances in technology to become more creative thinkers and deep learners?”

This shift in perspective aligns perfectly with the realities of the modern job market. Firms are massively investing in AI. Now, they want talented workers who understand the strengths and limitations of cutting-edge technologies. As educators, our role is to help students build AI-complementary skills. By reframing our approach to education, we can prepare students for the technological frontier.

My strategy is thus two-fold: teaching technological literacy, so that students use AI carefully and productively, while simultaneously changing my assessments so that they go beyond AI’s current capabilities, and instead assess students’ ingenuity.

My Two-Fold Strategy

1. Cultivating AI Literacy

Design classroom activities which integrate AI tools like Claude, e.g.:

• In pairs, ask Claude whether land reform was important for East Asia's industrialisation. Identify the limitations of this response and explain how it could be improved.

• Copy and paste Oliver Kim and Jen Kuan Wang’s Table 5 into Claude. Ask Claude to explain the regression table. Ask more questions until you understand.

• How did South Korea strengthen its domestic innovation? Draft an answer, then give it to Claude and ask for critique. Then improve on your answer.

Exploring Ideas & Getting Feedback with AI

In another class, I presented Sam Van Noort’s research industrialisation and democracy. He finds that the share of manufacturing employment is positively associated with democratisation.

Then I asked my students to form pairs and draft an essay plan:

“How might Communism affect the relationship between industrialisation and democratisation?”

Together, we discussed their answers.

Then I showed how they can ask these same questions to Claude, request feedback and continually brainstorm.

By engaging with Claude mid-lecture, evaluating its answers, and adding prompts, I demonstrated how to harness AI effectively.

3. Design Assessments That Go Beyond Current AI Capabilities

• Assume AI use: I take it for granted that some students will use AI in assessments, which is practically impossible to prevent, check, or punish consistently.

• Preventing plagiarism through innovative assessments: My questions require deep understanding of course material, careful analysis, and creative thinking.

• AI-proof questions: I’ve tested all my assessment questions against Claude, and it cannot produce answers worthy of a first-class grade.

• 3 hour or 48 hour online assessments recreate the conditions of the modern workplace, giving students to concrete task within a set deadline.

Sample Assessment Questions

These questions enable students to demonstrate their deep learning and creative thinking.

1. India’s Hindus have increasingly united to champion Hindu Nationalism. Indonesia’s Muslims, meanwhile, have pushed for jurisprudential Islam. How do these case studies challenge “The Narrow Corridor”?

2. Acemoglu and colleagues (2021) find that democracy increases growth, on average. Does it follow that poor countries should democratise? Explain why democracy may impede growth. Provide academic references.

3. Sub-Saharan African countries typically have = low agricultural productivity. Design a competitive bid for £50 billion – such as to research new technologies, provide infrastructure, tackle binding constraints in state capacity or rule of law, or trial new government policies. What would you do? And how would you assess its efficacy? Justify your answer and provide economic references.

4. What is the primary reason why Latin American countries did not emulate the East Asian model. Justify your answer and provide academic references.

5. How does evidence from historians like Cook and J.R. Neil challenge the conventional wisdom from political science about the roots of authoritarianism in the Middle East and North Africa?

6. Since studying this course, what beliefs about international development have you revised? Explain what evidence and justification led you to change your priors.

7. Identify the primary driver of low fertility in South Korea. Design an intervention that would tackle binding constraints. Provide justification and academic references.

Why These Questions Outsmart AI

1. AI-complementary skills: My assessments are designed to evaluate and encourage the development of skills that complement AI capabilities, such as critical thinking and creative problem-solving. Even if students copy and paste texts into Claude, it still struggles to think laterally. It will not necessarily recognise that,

   1. In democracies with low GDP per capita, a labour aristocracy may mobilise for protections that hurt international competitiveness and wider job growth.

   2. Environmental histories of MENA’s weak agriculture suggest states could not have been very strong (thereby challenging claims of religious authoritarianism, made by Faisal Ahmed, Ahmet Kuru and Jared Rubin).

   3. Hindu Nationalism is a good example of what Acemoglu and Robinson call a ‘strong society’. Hindus are increasingly united and cohesive, yet contrary to A&R’s expectations this may actually restrict the liberty of Muslims. In Indonesia, many Muslim organisations have increasingly organised for religious laws and institutions that actually restrict individual freedoms, especially for women (e.g. hijab mandates).

2. Course knowledge. Claude is good at listing all the possible factors, but cannot necessarily assess their relative importance, or binding constraints, especially as it does not necessarily have the latest evidence. My assessments will reward students who understand and critically evaluate the latest empirical literature, e.g. on South Korea’s ultra low fertility, or Sub-Saharan Africa’s agriculture and institutions.

3. Creative problem-solving: let students showcase their unique brilliance!

4. Justification and referencing: The requirement for academic references and justification ensures that students engage deeply with the literature and form well-reasoned arguments. Claude often makes up references, so this is a good signal of plagiarism.

Conclusion

I want to teach my students how to become brilliant - to develop AI-complementary skills and harness the latest innovations so they can be at the technological frontier and drive progress. That’s the human edge.

Personally, I also need to change. I need to get smarter and outwit AI, so that my assessments enable students to demonstrate their creativity and ingenuity. This way, I hope to embody the spirit of my field, driving progress through constant innovation!

P.s. this upcoming week, I have two awesome guests on my podcast: Oliver Kim (who has a brilliant new paper on Taiwan’s land reform) and my great friend Daron Acemoglu (back for the fourth time).

Further Resources

• Stanford’s advice on how to address and integrate AI (a good example of a university taking AI seriously)

• “Teaching effectively with ChatGPT” (if you are looking for an introduction to the basics)

University credentials have become almost mandatory for well-paid jobs, forcing young people into our classrooms. They’re compelled to wade through dry, jargon-laden papers on topics that often seem irrelevant.

So, how can we spunk things up? More importantly, how can we equip students with the skills to harness new technologies?

Embracing AI!

OK OK. Put down your pitchforks. I appreciate this is controversial. Some academics, juggling multiple commitments, prefer to maintain the status quo, ignoring the technological revolution happening around us.

But here’s my take: I want to help my students to leverage existing technologies, supercharging their analytical skills and making them more competitive in an era of AI. Plus, mountains of evidence suggest that, when it comes to learning, active engagement trumps passive scrolling. I’m optimistic that by constantly questioning, students can actually enhance their skills, knowledge and analysis.

Reimagining the Classroom

Let me share two examples from my upcoming course on the political economy of international development.

Consensus

At the start of each lecture, students will use Consensus, a search engine for academic papers. We’ll tackle big, unresolved questions like “How did East Asia become Rich?” Immediately, they’ll see the diversity of hypotheses in the empirical literature. Ever the devil’s advocate, I’ll introduce alternative theories – land reform, human capital, timing – sparking debate.

Plutarch said, “Education is not the filling of a pail, but the lighting of a fire”. And that’s my ultimate goal: to ignite curiosity through inquiry-based learning.

Maybe Consensus can help?

Claude

Instead of asking students to ‘summarise the readings’, we’ll use Claude, an AI assistant, to analyse papers and explore alternative hypotheses. Students will be encouraged to dig deeper, applying rigorous scrutiny to AI-generated insights.

For instance, we’ll feed Cristóbal Kay's classic paper, “Why East Asia Overtook Latin America: Agrarian Reform, Industrialisation and Development”: into Claude and probe its analysis.

Unsatisfied with brief one liners, I’ll encourage students to inquire more, such as by using Consensus to ask about land reform:

Or the impact of foreign aid:

But here’s the kicker: while Claude may raise intriguing hypotheses, it can also fabricate! I’ll emphasise that for assessments, every empirical claim must be judiciously referenced. So we now need to hunt down the data!

Truthfully, I’ve never tried this before. As you may know, I’ve spent the last year doing research in 8 countries, so this is just a naive ambition! But - like I say when baking - I have every reason for optimism! Hopefully, this approach will teach students to:

1. Harness new technologies

2. Develop curiosity through inquiry-based learning

3. Recognise and scrutinise competing hypotheses.

Let me know what you think, or what you’ve trialled in your own classrooms. This is a brave new world, and as with all things, we learn through experimentation.

But please don’t forward this essay, as Daron will probably unfriend me! (Joke! But really..)

My Syllabus Guide:

I will also provide written guidance to students, in order to provide a level platform:

Related Essays:

• ‘How can professors prevent plagiarism in an world of ChatGPT?’.

Stay tuned for updates to my “Tiny Textbook on the Political Economy of International Development”.

If you have not already devoured it, I strongly recommend Ken Bain’s tremendous new book on how to cultivate deep thinkers and curious minds. Terrific!

1. Ignore, continue as before. Credentials will then become worthless.

2. Ethical agreements can be signed, promising no plagiarism. But we know this is bogus. The ghost-written essay business is booming.

3. Trick students. One professor adds ‘trojan horses’. In essay instructions, he’ll covertly write in white, ‘use the word insouciance’.

4. Presume we’ll be able to tell. But such arrogance is misplaced. Gen-Z is super smart and tech-savvy. Claude is now very advanced, always ready to vary the style, and even write like Ernest Hemingway.

5. Paper, in-person exams. This seems akin to banning calculators. If we train students for the Stone Age, we should expect zero improvement in innovation.

Is Claude.AI really that good?

Let me show you an experiment. I copied and pasted “The Real Politics of the Horn of Africa: Money, War and the Business of Power” by Alex de Waal into Claude, which gave a superb summary within 5 seconds. I further asked specific questions. Again, Claude excelled.

What’s our goal?

Academic discussions about how to eliminate plagiarism seem rather frustrating. Some professors are effectively saying “How can *I* can continue as before?” “How can I teach the same things and check they’re not cheating with AI?”.

Personally, I’m extremely sceptical of repressive attempts to hammer down rules, dictating to students that they must not use the latest innovations. As advanced economies struggle with slowdowns in productivity, don’t we want to encourage open-minded exploration of the technological frontier? Isn’t that how we make progress?

So, what if we asked a different question:

“How can I enable my students to harness advances in technology to become more creative thinkers and deep learners?”

Skills in synthesis and analysis are absolutely vital, as is the creative ability to harness new technology. So how can we support students to build core skills, while also capitalising on AI?

Suggestions

I teach on the Political Economy of International Development (you can read my textbook here). I think there are several options:

1. Audio guides for core texts.

   1. Academic texts are often terribly impenetrable (sometimes intentionally so). We should not be surprised if students (studying in their 2nd or 3rd language) use software which make convoluted language more legible.

   2. I make 15 minute audio guides for each core text. Students listen as they read, pausing to respond to my suggestions, e.g. “summarise the methodology, what are its strengths and limitations?”. These are extremely popular, and help students build core skills in synthesis and analysis.

2. Encouraging students to use technology.

   1. This has several advantages. First, it’s a leveller: ensuring all students are equally knowledgeable about available technology. Second, it actively encourages gains in productivity, exploiting the latest technologies. Third, Claude gives excellent feedback, identifying where the evidence and justification are weak. This is highly valued - as students generally complain about poor quality feedback.

   2. “What’s the primary reason why East Asia got rich?”, “Why is violence so high in Latin America?” Write an essay answering one of these questions, then ask Claude to highlight the limitations. Improve the essay, provide better evidence, and submit along with your conversation with Claude.

3. Rocking Our Priors. We could encourage students to reflect on new evidence and explain how they updated their beliefs.

   1. In Lecture 1, I’ll ask students to discuss in pairs, “Why do you think some countries are poor, while others are rich? What’s the core driver?”, “How did East Asia become wealthy?”, “And what do you think is the most important priority for international development today”. Using online polling, students will submit their answers.

   2. At the end of the course, I’ll ask them to write an essay, “In studying international development, how have you revised your priors? What did you previously think; what had you overlooked? What data and reasoning was persuasive?”. This encourages reflection and engagement with evidence.

   3. For guidance, I will share my previous post, “3 things I got wrong about patriarchy”, as well as my podcast “What did Acemoglu get Wrong?.

Alternative ideas and suggestions are very welcome!

Further reading

“Super Courses: The Future of Teaching and Learning” by Ken Bain. (He does not discuss AI, but it’s a really brilliant book on teaching and learning more broadly).

Related episodes of Rocking Our Priors

“Power and Progress” with Daron Acemoglu

“What did Acemoglu get Wrong?, with Daron Acemoglu.

Stay tuned for my upcoming episode with Daron, discussing culture.