AI and Education - Part 2, The Challenge of Vibe Learning

🧠 Knowledge vs. Information

The current wave (or bubble) of AI applications is dominated by skin-deep (surface) applications. They are simple, accessible and serve as an entry point for students and educators alike.

The applications we are seeing in this initial phase (including those showcased by Google and OpenAI) prioritise information, rather than knowledge. So what is the difference?

In the context of education, there are many opinions, but for this article, we’ll use the following definitions:

• Information: the what, it is syntactical and has no reference to what it is about. It can be facts and procedures. Information can exist in a vacuum.
• Knowledge: the why, it is semantic and uses information to organise and provide detail. It is how information can be used to frame decisions.

The trend in the base generalist systems is information. Our challenge is to move to knowledge. This is not a new problem; we remember (well, those born in the 80s and 90s) the release of Encarta on CD-ROM! Just as AI has evolved, the encyclopedia on a CD made information acquisition trivial; however, knowledge acquisition still relies on the human interface (currently).

📖 Prompt-Based Learning

Vibe Learning uses prompt-based learning to facilitate learning. Rather than being a studied and formal pedagogical theory, it is a technical implementation of a learning strategy.

The prompt-based learning leverages Natural Language Processing (NLP) and the power of Large Language Models (LLMs) to perform tasks, aiming to learn and understand complex concepts.

The shift that prompt-based learning forces in students is to formulate prompts rather than internalise knowledge and develop skills. This technical paradigm produces “vibe learning”. Students now learn by generating prompts to gather the information.

The “vibe” or the intuitive feel of getting a quick, coherent answer and the satisfaction of a complete answer with limited cognitive struggle means students love this form of learning.

Prompt Engineering & Literacy

The focus of education in this method moves from answering questions to building prompts. The prompt literacy becomes the “education”. We have already seen the rise of prompt engineering articles, guides and snake oil.

This is an important skill (I use it myself, and many of our applications rely on it). Still, the conversation needs to include the fact that we are limiting our knowledge acquisition by moving to a vibe-based learning approach.

Cognitive Offloading

The tension of vibe learning lies in its cognitive implications. AI’s ability to generate content and provide structured, detailed information leads to cognitive offloading. Cognitive offloading is a process that utilises external tools to reduce our own mental effort.

When a student uses AI to generate an essay outline or summarise a complex text, they are offloading cognitive tasks that are essential for deep learning, such as active recall and problem-solving.

This creates a "cognitive paradox" of AI in education: the technology has the dual capacity to both enhance and erode cognitive functions. Over-reliance on these tools leads to a weakening of long-term memory and a decline in critical thinking abilities.

Paradoxically, the same AI tools can be facilitators of inquiry-based learning, a pedagogical approach that encourages students to ask questions, investigate, and construct their own understanding. The specific implementation of the systems will govern this. The issue we face is that generalist applications, such as ChatGPT, Gemini, Co-Pilot, and Claude, tend to lean towards embracing cognitive offloading.

😵‍💫 Vibe Learning - formal definition

We need to understand that, like anything in this world, the concept of vibe learning is a nuanced concept that exists on a spectrum.

• At one end, superficial cognitive offloading, where the AI is used as a substitute for critical thinking and skill development. This is characterised by vague prompts, uncritical acceptance of AI output, and an instrumental goal of task completion.
• At the other end, sophisticated AI-assisted inquiry, where the AI is used as a tool to augment and accelerate human-led exploration, creativity, and knowledge construction. This is characterised by specific, context-rich, iterative prompting and a critical, reflective engagement with the AI's output.

The type of interaction with an AI will dictate the level of cognitive offloading, and also, as we’ll see, the ability to build knowledge.

An essential aspect of vibe learning is the prompts being used. The prompting becomes the artefact of learning. The prompts become your work, and the prompts show the level of understanding or knowledge.

🕵️ The Dichotomy of Vibe Learning: Benefits and Detriments

Vibe learning offers the promise of the modern education revolution.

• Personalised learning
• Efficiency
• Creative exploration
• Equitable access to education

But on the other hand, it also creates potential for harm:

• Academic integrity
• Cognitive development
• Skills development
• Access to education

Let’s break down the benefits and potential problems:

The Erosion of Critical Thinking and Cognitive Skills

Over-reliance on AI will hinder the development of critical thinking. Students may passively accept AI-generated content without the critical scrutiny necessary for genuine learning, a behaviour that can lead to the adoption of misinformation and biased perspectives.

To better understand the nature of learning and how we at Intuition implement learning, you can read our explanation of the Intuition 3-step Cycle

Cognitive science research suggests that while AI enhances access to information, it may weaken long-term memory retention by reducing the need for cognitive processes, such as active recall.

AIs help with information acquisition but hinder knowledge acquisition.

Academic Integrity in Crisis

Generative AI has precipitated the next crisis in academic integrity. The ease with which students can produce essays, reports, and other assignments without engaging in the learning process challenges our notions of authorship and originality.

As AI models become more sophisticated, their output increasingly mimics human writing styles, rendering detection tools unreliable and creating a "cat-and-mouse game" that educational institutions are currently losing, with many struggling to adapt.

We’re seeing institutions using AI detection technology that produces false positives and can significantly impact the education and well-being of students.

The Perils of the Black Box

Relying on AI tools introduces inherent risks related to the technology's inherent nature. LLMs are known to "hallucinate"—that is, to generate inaccurate information, logical inconsistencies, and even fabricate references and citations.

The black box nature of the LLMs further challenges our ability to handle the cognitive offloading.

Beyond the content itself, the use of AI in education raises ethical concerns. Chief among these concerns is the issue of data privacy, with one poll showing that nearly 70% of parents oppose granting AI software access to their children's personal and academic information. However, little do they realise that they are already using their information and have their data.

📝 Assessment in the Vibe Learning: Challenges and Adaptations

The drive to use vibe learning and the problems associated with cognitive offloading are, in large part, driven by the nature of assessment.

The current traditional form of assessments and homework (practice components) has become a chore to many students. It becomes “ticking” the box rather than genuine learning. But the students can’t be blamed here; the culprit is the nature of testing and assessment. A change like assessment is required to maximise the benefits of AI.

In addition, students prepare for exams (written, non-open-book), and they learn by vibe; they cognitive offload. The question is, are they ready for their exams, and do they need to be?

In the last article, we saw that one concern is the way in which students interact with the AI, often in a subservient tone. This creates a vague, poor form of promotion and disconnection; the language and tone they use force them to the superficial, cognitive offloading end of the vibe learning spectrum.

The Obsolescence of Traditional Assessment

What is essential in assessing the outcome, the skills, or the mark? Traditional assessment and education systems care about the mark. The HSC (in NSW) and the ATAR blocking university access are based on marks, and the connection to outcomes is documented. However, the nature of assessment and pressures means that students will fall into cognitive offloading if it means their marks (and hence their ATAR) will improve.

We, as a society, have incentivised this approach. Our experience at Intuition is that parents and many students would value a mark rather than learning. It’s a challenge to work within the system, but also realise that long-term learning and skills are the most effective way.

AIs as a technological disruption, also challenge a long-held definition of learning itself. For decades, a dominant view in education has been that learning is "a change in long-term memory". Generative AI can perfectly mimic the output of this type of learning; it can "recite" facts, synthesise information, and produce structured text as if it "remembers" them.

A Retreat to Proctored Environments

Many learning institutions are moving to older forms of assessment:

• In-person, handwritten exams: The revival of the exam requires students to formulate and write essays under timed, supervised conditions, using only pen and paper. This is still how the HSC and most standardised exams are in High School.
• Oral examinations and defences: Gaining significant traction, oral exams require students to articulate their understanding of a subject in person, engaging in a dialogue with instructors that probes their depth of knowledge and reasoning abilities.
• Socratic dialogues and in-class discussions: These methods emphasise real-time engagement and critical thinking, assessing a student's ability to think on their feet and build arguments collaboratively.

The problems with these methods are that handwritten exams, while still widely used, are costly to run, difficult to manage, and the question is whether they really test ability.

Designing for the Future: Authentic and AI-Integrated Assessment

The solution lies in embracing the technology and using it. Vibe learning means we need to redesign assessments to embrace it.

AI-Inclusive Assessment

Rather than attempting to ban AI, many educators are developing assessments that strategically and transparently incorporate it as a tool for learning. This approach not only mitigates cheating but also teaches students crucial digital literacy skills. Examples of AI-inclusive strategies include :

• Critical AI analysis: Assigning students to generate a response from an AI on a specific topic and then write their own analysis critiquing the AI's output for accuracy, bias, logical fallacies, and missing perspectives.
• AI as a brainstorming partner: Allowing students to use AI to generate initial ideas or an outline, but requiring the final product to be written independently, often accompanied by a reflection on how the AI was used.
• Combining human and AI feedback: Structuring peer review processes where students first receive feedback from an AI tool and then from their peers, followed by a reflection on the differences and relative value of each type of feedback.
• Requiring an "AI usage statement": Promoting transparency by asking students to submit a brief statement with their assignments detailing which AI tools they used and for what purpose.

Modern AI-Powered Assessment

Beyond adapting to student use of AI, educators can also leverage AI to improve the assessment process itself. Modern AI-powered assessment tools can shift the focus from summative judgment to formative growth by providing :

• Adaptive quizzing: AI can generate practice quizzes that adjust in difficulty based on a student's performance, providing personalised retrieval practice.
• Gamification: AI can be used to design engaging, game-like assessments that increase student motivation and knowledge retention.
• Automated, personalised feedback: AI can analyse student work to identify common errors and provide instant, constructive feedback, freeing up instructor time for more in-depth, higher-level engagement with students.

Ultimately, the challenge of assessment in the age of AI is an opportunity to realign evaluation methods with the most important goals of education: fostering deep understanding, critical thinking, and the ability to apply knowledge in meaningful ways.

🙋 The Future of Pedagogy

The emergence of "vibe learning" is not an endpoint, but a catalyst for reimagining educational roles, goals, and philosophies.

The disruption to traditional assessment is merely the most visible symptom of a more profound transformation. As AI automates the delivery of information and the execution of routine cognitive tasks, we need to move towards the acquisition of knowledge.

AIs continue to improve and develop, and the role of education will be to promote knowledge acquisition.

The Evolving Role of the Educator: From Sage to Guide

The “sage” model of education has undergone significant changes over the past 30 years, yet it still forms the basis of many learning institutions. Information transmission is becoming faster, easier, and more accessible. The educator’s role is shifting to that of a guide. The traditional teaching framework has the teacher having multiple roles:

• Content creator
• Lecturer
• Curriculum designer
• Assessor
• Mentor.

Many of these functions, particularly those related to content creation (e.g., generating lesson plans, quizzes) and information delivery (e.g., virtual tutoring),

The educator's role is evolving into that of a "guide on the side" or an "architect of learning experiences". In this new paradigm, the educator's primary responsibilities are to:

• Foster critical thinking: Guiding students to question, analyse, and critique information, including AI-generated content.
• Model ethical AI use: Teaching students to use these powerful tools responsibly, transparently, and ethically.
• Facilitate complex problem-solving: Designing authentic, collaborative learning experiences that challenge students to apply knowledge in novel contexts.
• Cultivate human-centric skills: Nurturing capabilities that AI cannot replicate, such as empathy, collaboration, ethical judgment, and emotional intelligence.

Curricular Imperatives: The New Foundational Literates

Curricula need to be updated to accommodate the use of AI. Governments are slow to act, and rather than embracing and dealing with AI, they tend to move towards restricting or controlling it, but they are ultimately fighting a losing battle. Instead, changes to the syllabus need to be implemented quickly. Universities have more ability to adapt, but again, the vibe learning approach has the potential to fall into the negative, and we’re left with students who lack critical thinking and skills, can obtain information, but lack knowledge.

This new literacy goes beyond simply teaching students how to use AI tools. A comprehensive approach must include :

• Conceptual Understanding: Teaching students the basic principles of how generative AI works, including its reliance on large datasets and probabilistic models.
• Critical Evaluation: Equipping students with the skills to critically evaluate AI output for accuracy, bias, and limitations.
• Ethical and Societal Implications: Fostering an understanding of the broader societal impacts of AI, including issues of data privacy, job displacement, algorithmic bias, and its relationship with human rights.

AI and Constructivism: A Tool for Knowledge Building

This is our challenge. "Vibe learning" can manifest as the passive reception of information; AI tools, when integrated thoughtfully, are uniquely suited to power a constructivist pedagogy. Constructivism is a learning theory that posits that learners actively construct their own knowledge and understanding through their experiences and interactions with the world, rather than passively receiving it. AI is the tool to build this, but it relies on the effective use of AI and vibe learning.

At Intuition, our goal is to promote learning and knowledge through Intu AI. We’re doing this by introducing the following (some are already being used, while others are in development as of Sep 2025)

• Tutoring personas: build a relationship with the tutor to encourage better prompting; recall that the prompts are your work in Vibe Learning.
• Socratic Instructions: Building the prompts and interaction is fundamental to moving from information to knowledge.
• Quizzes: Custom quizzes that will eventually adapt to the student’s learning to better test their knowledge rather than information retrieval.
• Remove busy work: Eliminate the need for students and educators to perform tedious tasks such as planning and marking, allowing them to focus on the human connection.

Change assessment to use AI to assist in the large-scale applications of

Conclusions

As we’ve always done, we’re adapting to the new generation and the new changes. Not everything is clear-cut, and in fact, mostly it's not. However, the changes we’re making are outcome-driven; we want to help students achieve their goals, as well as develop skills that will last beyond high school. This has never been more critical, as we are witnessing a workplace revolution catalysed by AI, but driven by the need for productivity.

Vibe learning is a spectrum; the current state tends to lean toward the superficial “skin-deep” applications that promote cognitive offloading. The challenge we have as educators and developers is to move it to the sophisticated knowledge-seeking side. Easier said than done.

In the next part of this series, we’ll change our approach to the Agentic AIs paradigm and how it can be used in an educational context.

References:

• Benefits of AI - Washington State University
• AI in the classroom - Cornell University
• AI and Inquiry - AVID
• The cognitive paradox of AI in education: between enhancement and erosion (2025)
• AI for Learning Assessments: 6 Modern Approaches to Boost Student Success in 2025
• World Economic Forum: The future of learning: How AI is revolutionizing education 4.0 (2024)
• Harnessing AI to Power Constructivist Learning: An Evolution in Educational Methodologies (2024)