Tag Archives: GenAI

GenAI for Gen Z: Rethinking studying with GenAI

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If you dont have time to read this months blog – listen to my AI alter ego summarise the key points.

Anyone falling into the Gen Z category would have been born between 1997 and 2012, meaning they range in age from 13 to 28. They are the first true digital natives, having grown up with the internet, smartphones, and social media as integral parts of their lives. This constant connectivity has shaped their worldviews, making them, in theory tech-savvy, innovative, and quick to adapt to new digital tools and platforms.

Gen Z is at the forefront of Generative AI adoption (GenAI), with high usage rates across various platforms. Whilst it’s hard to get exact numbers especially those of school age, a poll undertaken by the Higher Education Policy Institute, said that 88% of undergraduates had used GenAI over the past year,  and 25% said they had used AI-generated text to help them draft assessments. Most however “said” they were using ChatGPT and similar tools to explain concepts, summarise relevant articles or suggest ideas.

67% agreed that it was essential to understand how to use AI and yet only 36% said that they had received support from their university

Like it or not GenAI is becoming an integral part of learning, studying and assessment. And that’s why there needs to be more guidance on its use, welcome to this month’s blog – Rethinking studying with GenAI.

Tasks not Tech
The common mistake people make with technology, in this instance GenAI is they get caught up with what it can do and start to look for ways they can use it. This is perfectly logical, but not the best approach – it’s a great example of:

“If all you have is a hammer, everything looks like a nail.”

Far better is to identify a specific task and decide how you might save time without reducing the effectiveness of learning. For example, using GenAI to generate ideas for an essay is time consuming, and whilst it could be argued that you can learn from almost any activity, generating ideas does not significantly improve learning. Contrast this with actually writing the essay. This also takes time but the learner will have to structure their thoughts, challenge ideas, and produce an answer that not only makes sense to them but to others. It is this type of mental gymnastics that helps people learn and should never be substituted by GenAI.  

Using GenAI to study more effectively
Here are some of the best ways of transforming your study approach by leveraging GenAI. Importantly, these methods are designed to enhance learning without compromising the depth and quality of understanding. Note Generative AI (GenAI) is a type of artificial intelligence that creates new content. A GPT is a specific kind of GenAI e.g. ChatGPT and Claude.

1. Planning

  • Personalised Study Planning – Use ChatGPT, Claude etc to create a tailored study timetable based on exam dates and your personal strengths and weaknesses. This can be as detailed as you like, but because it’s personalised the GPT could for example allocate more time to the areas you find more difficult. Another level of sophistication might be to prioritise your time on the more examinable topics.
  • Identifying examinable areas – Analysing past papers and reading examiners guidance and reports has long been a top study tip. This is an ideal task for a GPT, get the prompt right and it will even put it into a table for you.

2. Reducing cognitive load (information overload)

One of the challenges in learning is coping with the large amounts of information you need to learn. GenAI can help by:

  • Chunking content – This is a hugely effective technique, just ask the GPT to break down complex ideas into smaller, digestible parts.
  • Filtering information – A lot of time can be spent reading documents not really learning because you are only skimming looking for key words or phrases. GPT’s are excellent at extracting key information from documents, videos, and even discussions.
  • Visual aids – A picture paints a thousand words, ask the GPT to provide information in different formats for examples a diagram, chart, table, or mind map.

3. Summarising

This is similar to chunking but more specific, all you need to do is add more guidance when designing the prompt. This is not about reducing content, but capturing the essence of what is being said.

  • Summarising large amounts of text or articles – Asking a GPT to summarise an article may be a little vague. Be more specific, by stating how many words you want it to use or asking it to focus on five key areas. You can also ask it to summarise the information in a style easier for the individual to consume, perhaps for someone with dyslexia.
  • Note taking from video – In addition to YouTube, many organisations now offer video recordings of lectures, webinars, and presentations that can be watched later. A GPT can scan the transcript and summarise the key points, even offering alternative explanations if it was not clear.
  • Forum conversations – Whilst a forum is an excellent way of engaging with others, it’s possible you don’t have the time. A GPT can easily summarise a thread picking out the main points.  

4. Improving understanding

  • Breaking down complex topics – Although this also reduces cognitive load, breaking down complex topics is an excellent way of helping to gain a better understanding of a subject or topic. One of the bests ways to do this is by asking the GPT to break the problem down into a series of steps. You could also ask it to explain the topic to someone who has only recently started the course, or maybe even a 10 year old.
  • Ask questions – This is possibly the most popular use of a GPT, simply asking it a question. For example, what is the capital asset pricing model and can you give me an example? The best part is you can ask follow up questions if you don’t understand the first time.
  • Teach me – Why not ask the GPT to teach you the topic.  It might be easier to use one of the custom GPT’s that’s been specifically designed to do this, for example check out Universal Primer. Some of these can act as a coach and not simply give you the answer but ask you further questions to test your understanding and then provide feedback.

5. Creating study materials

One of the most powerful ways to use GPTs for studying is to generate structured, personalised materials tailored to your learning needs.

  • Flip cards – This can be time consuming but not for a GPT. They can generate question and answer pairs from textbooks, lecture notes, or past exam papers. For example, “Create 10 flashcards on Modigliani and Miller’s capital structure theory.”
  • Study notes and revision notes – Many learners struggle with condensing large volumes of information into digestible summaries. GPTs can simplify this process by extracting key points, breaking down complex ideas, and even adapting explanations to suit your level of understanding.
  • Mind maps – Mind maps help learner see relationships between concepts, improving comprehension and recall. GPTs can create structured outlines that you can easily convert into visual diagrams. For example, “Create a structured mind map for managing foreign exchange risk, including hedging strategies.”
  • Writing questions and designing quizzes – One of the best ways to test knowledge is through self-assessment. GPTs can generate quizzes tailored to your own study material, which can range from basic recall to complex scenario-based questions. For example, “Generate five exam style exam questions on interest rate risk, including one scenario-based question.”

6. Assessments and Feedback

This section highlights situations were using a GPT is not advisable, particularly when answering questions designed to test your understanding and promote learning. While a GPT can generate correct answers, it does not facilitate the deeper cognitive processes in the leaner for them to fully grasp concepts.

Another concern is that GPTs can occasionally produce answers that, although plausible, may be incorrect or oversimplified. This is especially problematic in technical subjects, where even small errors can lead to significant misunderstandings and hinder a student’s ability to apply knowledge accurately.

But they can be used to help

  • Plan the answer for your assessment – GPTs can be an invaluable tool for structuring and planning your response to an assessment. They can help you break down complex questions, identify key themes, and organise your thoughts logically, which can significantly improve the quality of your answer. However, it’s essential to use GPTs responsibly by focusing on the planning and brainstorming phase rather than relying on them to generate the entire response.
  • Providing feedback – A GPT can be a powerful tool for providing feedback, offering valuable insights to help improve the quality of your work. Whether you’re refining an essay, reviewing a problem set, or crafting a project, GenAI feedback can highlight areas that need attention, suggesting ways to enhance clarity, depth, and structure. However, it’s important to approach this feedback with a critical and reflective mindset to ensure it leads to genuine learning and improvement, rather than simply accepting it at face value.

Conclusions
This is not an exhaustive list, but I hope it has inspired you to explore these tools and use them responsibly. GenAI can be a powerful aid in helping you learn more effectively, from breaking down complex topics to enhancing revision and providing feedback. However, its true value goes beyond simply learning, by using GenAI thoughtfully while studying, you are also developing essential digital skills that will be increasingly important in the future of work.

Predicting Learning in 2025 +

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Making predictions is of course a mugs game. Most people start with what they currently know and project forwards using logic to justify their conclusions. This however leads to our first problem – “you don’t know what you don’t know.”

Secondly, a prediction is more likely to be true if the environment is stable, and that leads to problem number two – we are living in hugely unpredictable times. Technology, in particular AI is moving so fast it’s hard to keep up, politically there is both change and instability, making it difficult to say with any certainly what policies or regulatory requirements will come into effect, and the climate is shouting at us, although we don’t seem to be listening.

And yet it’s still worth making predictions, not so much to see if you can get it right, but to play around with what the future might bring, take advantage where you can and make changes or at least warn others if you don’t like what you see. 

The best way to predict the future is to create it. Peter Drucker

So here goes – what might happen this year in the world of learning? 

1.Learning will not change: But learners will adapt to different ways of studying
While the world around us continues to change, the fundamental way we learn as humans remains largely unchanged. Despite advancements in AI, neuroscience, and educational tools, the core processes of how our brains absorb and retain information are rooted in biology and consequently, relatively stable. But learners will begin to adapt to this new world, and some will take full advantage of what these new technologies have to offer.

A new world problem Cognitive “offload” – Who is doing the heavy lifting – the machine or the human?

“There are no gains without pains” Benjamin Franklin

However, there will be those who fall into the trap of taking the easy way out and using the technology to “offload” learning, and as a consequence, learn very little. My prediction is that we will see far more people offloading learning in 2025, which is clearly a concern!

2.AI (GenAI) will continue to dominate: Bye Bye Text Books
Possibly the easiest prediction is that AI will dominate. Almost every day we are met with a new model that is easier to use, providing more effective ways of answering questions, summarising complex information and responding with high quality opinion. By the end of 2025 AI (GenAI) will have firmly established itself as a tool for learning, offering instant access to vast amounts of information. Traditional textbooks will become increasingly less valuable as students and professionals turn to large language models to provide real-time answers and explanations.

But AI’s capabilities extend far beyond simply generating content.  We will also see the expansion of the use of chat bots (study buddies) to not only answer questions but, provide coaching, motivation, and personalised feedback. The natural progression for these “study buddies” is that they will develop into “intelligent agents/tutors.” Agents are more autonomous and can perceive the environment, process information, and take actions to achieve specific goals. This means they will be able to analyse individual progress, suggest next steps, adapt materials in real time, and offer tailored support.  

3.Sector disruption: Content, assessment, and the thirst for data
The focus here is on the educational publishers, institutions that produce textbooks, workbooks, teacher guides and even digital learning platforms. With AI being capable of generating high-quality content quickly and at scale, traditional content providers will need to rethink their business models. Although there is unlikely to be a significant impact in 2025, we will begin to see changes in how some of these businesses operate. The focus will shift from storing knowledge inside “books” to recognising the value is in curation and providing meaningful learning experiences, organising knowledge into effective sequences and simplifying complex topics to support deeper understanding.

Assessments are in need of significant transformation and we will increasingly see calls for alternative methods of assessment to be used. This is mostly driven by concerns around plagiarism, but AI brings some interesting and arguably more robust ways of testing knowledge and skills. For example, AI-driven adaptive testing, offering real-time performance analysis and personalised assessments that move beyond standardised testing. Skills will remain high on the agenda this year with even more pressure being applied on educators to encourage them to close the gap between what is taught in the classroom compared to what is required in the workplace.  A change in assessment, perhaps using real world simulation to assess these skills, could be part of the solution.

It is easy to get carried away with predictions and forget about some of the reasons they may not come to fruition. One such barrier is that organisations will struggle to get their data in one place to provide meaningful information for these models. 2025 will see organisations spending significant amounts of both time and money cleaning and tagging data so that it is useful

“The Internet will collapse in 1996″ Robert Metcalfe”. 1995

4.Regulation, the green agenda, and commercial pressures
Speaking of barriers, by the end of 2025, regulatory frameworks for AI in education will be far more developed but the landscape for adoption is likely to be patchy. Governments and educational bodies both in the UK and around the world will seek to strike a balance between innovation and ethical concerns, ensuring that AI-driven learning tools are used responsibly. The environmental impact of AI will become an increasingly critical consideration, with growing awareness of the substantial energy consumption required to train and run large AI models. These high energy costs add complexity to discussions about AI’s role in education.

Commercial interests and financial investments will heavily influence the direction of policy. Big technology firms will continue to play a major role in shaping the future of education, with their AI tools becoming more deeply embedded in learning institutions. As AI becomes an integral part of the education ecosystem, the debate will centre around who controls the technology and how it can best serve learners rather than corporate interests, all while addressing the significant environmental footprint of the AI infrastructure.

“Subscription models for music are bankrupt”. Steve Jobs, 2003

Summary
I have summarised the key points below, briefer of course but as a consequence less nuanced. Will they come true, maybe but as Yogi Berra once said – “It’s tough to make predictions, especially about the future.”

  • Prediction 1 – learning will not change but learners will. They will begin to adapt, changing how they study, the problem is this could lead to them not learning at all.
  • Prediction 2 – AI (GenAI) will continue to dominate. This will lead to the demise of the text book, the development of GenAI study buddies and in time, intelligent agents/tutors.
  • Prediction 3 – Watch out for  sector disruption, especially for educational publishers. In addition, assessments are due for a revamp but data will remain king.
  • Prediction 4 – Regulation will be in conflict with innovation. There will be a growing tension between regulation and the responsible use of AI with commercial organisations having the space to innovate.

Transforming Learning – GenAI is two years old

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ChatGPT – Happy second birthday
Generative AI (GenAI), specifically ChatGPT exploded onto the scene in November 2022, which means it is only two years old. Initially people were slow to react, trying to figure out what this new technology was, many were confused, thinking it was a “bit like Google.” But when they saw what it could do – “generating” detailed, human-like responses to human generated “prompts,” ideas as to what it could be used for started to emerge. The uptake was extraordinary with over 1 million people using it within the first five days, a year later this had grown to 153 million monthly users and as at November 2024 its around 200 million. The use of GPTs across all platforms is difficult to estimate but it could be something in the region of 400 – 500 million. That said, and to put this in perspective, google search has over 8.5 billion searches every day, that’s the equivalent to the world’s population!

From Wow to adoption
Initially there was the WOW moment, true AI had been around for a long time but GenAI made it accessible to ordinary people. In the period from November 2022 to early 2023 we saw the early adopters, driven mostly by curiosity and a desire to experiment. By mid 2023 it became a little more mainstream as other GPTs emerged e.g. Googles Bard (Now Gemini), and Microsoft’s Copilot to name just two. But it was not all plain sailing, ethical concerns began to grow and by the end of 2023 there were people talking about misinformation, problems with academic integrity, and job displacement. This led to calls for greater regulation especially in Europe, where AI governance frameworks were developed to address some of the risks.

In terms of education, initially there were calls to ban learners from using these tools in response to answers being produced that were clearly not the work of the individual. And although many still worry, by early 2024, there was a creeping acceptance that the genie was out of the bottle and it was time for schools, colleges, and universities to redefine their policies, accept GPTs, and integrate rather than ban. 2024 saw even greater adoption, according to a recent survey, 48% of teachers are now using GenAI tools in some way.

GenAI – Educational disrupter
There have been significant changes in education over the last 50 years e.g. the introduction of personal computers and the Internet (1980s -1990s), making content far more accessible, and changing some learning practices. Then in the 2000 – 2010s we saw the development of E-learning Platforms and MOOCs such as Moodle, Blackboard and Coursera. This fuelled the growth of online education providing learners with access to quality courses across the globe.

But I am going to argue that as important as these developments were, not least because they are essential underpinning technologies for GenAI, we are always “standing on the shoulders of giants” – GenAI is by far the biggest educational disrupter than anything that has come before. Here are a few of the reasons:

  • Personalised Learning at scale: GenAI tools make it possible for everyone to enjoy a highly personalised learning experience. For instance, AI can adapt to an individual’s learning style, pace, and level of understanding, offering custom explanations and feedback. This brings us far closer to solving the elusive two sigma problem.
  • Easier access to knowledge and resources: Although it could be argued the internet already offers the worlds information on a page, the nature of the interaction has improved making it far easier to use, and have almost human conversations. This means learners can now explore topics in depth, engage in Socratic questioning, produce summaries reducing cognitive load and be inspired by some of the questions the AI might ask.
  • Changing the Teachers role: Teachers and educators can use GenAI to automate administrative tasks such as marking and answering frequently asked questions. And perhaps more importantly the traditional teacher centered instructor role is shifting to that of a facilitator, guiding students rather than “telling” them.
  • Changes the skill set: Learners must rapidly enhance their skills in prompting, AI literacy, critical thinking, and foster a greater level of curiosity if they are to remain desirable to employers.
  • Disrupting Assessment: The use of GenAI for generating essays, reports, and answers has raised concerns about academic integrity. How can you tell if it’s the learners own work? As a result, educational institutions are now having to rethinking assessments, moving towards more interactive, collaborative, and project-based formats.

Transforming learning
GenAI is not only disrupting the way learning is delivered its also having an impact on the way we learn.

A recent study by Matthias Stadler, Maria Bannert and Michael Sailer compared the use of large language models (LLMs), such as ChatGPT, and traditional search engines (Google) in helping with problem-based exploration. They focused on how each influences cognitive load and the quality of learning outcomes. What they found was a trade-off between cognitive ease and depth of learning. LLMs are effective at reducing the barriers to information, making them useful for tasks where efficiency is a priority. But they may not be as beneficial for tasks requiring critical evaluation and complex reasoning. Traditional search engines, need the learner to put in far more effort in terms of thinking, which results in a deeper and better understanding of the subject matter.

The research reveals a fascinating paradox in how learners interact with digital learning tools. When using LLMs, learners experienced a dramatically reduced cognitive burden. In other words, they had far less information to think, making it easier to “see the wood from the trees.” This is what any good teacher does, they simplify. But because there was little effort required (desirable difficulty) they were less engaged and as a result there was little intellectual development.

This leads to one of the biggest concerns about Generative AI, the idea that it is seen as a way of offloading learning – the problem is you cant.

Technological convenience doesn’t automatically enhance learning.

Conclusions
As we celebrate ChatGPT’s second birthday, it’s clear that GenAI is more than a fleeting novelty, it has already begun to disrupt the world of education and learning. Its ability to personalise learning, reduce cognitive barriers, and provide a human friendly access to resources holds immense potential to transform how we teach and learn. However, the opportunities come hand in hand with significant challenges.

The risk of over-reliance on GenAI, where learners disengage from critical thinking and problem solving, cannot be ignored. True learning requires effort, reflection, and the development of independent thought, skills that no technology can substitute.

The role of educators is crucial in ensuring that GenAI is used to complement, not replace, these processes.