Why AI makes traditional education models obsolete – and what to do about it

The rise of generative artificial intelligence (AI) tools is transforming the higher education landscape, sparking both excitement and concern. While many celebrate their potential to revolutionize education, others caution that they will quickly erode academic integrity by enabling wide-spread cheating and plagiarism.

While these concerns are valid and understandable, they overshadow a more critical dilemma: the educational model that dominates global institutions is outdated and fundamentally unprepared for the age of AI.

A new model is urgently needed. One grounded in learning science and primarily focused on teaching students “how to think”' through the cultivation of “durable skills” such as critical and creative thinking, ethical reasoning and emotional intelligence. This would not only future-proof students by allowing them to do what AI cannot but also enable them to ethically and effectively utilize those tools and avoid being sidelined by them.

If we are to cultivate highly effective learners who can transfer skills from the classroom to the real world, we need to ensure they actively recall and deliberately apply information across both time and context. They also need ongoing, constructive feedback that addresses gaps in their understanding.

There are four reasons why the most popular and prevalent educational model (lecture plus high-stakes exams) fails to meet such conditions:

AI not only highlights the shortcomings of a traditional lecture-and-exam model but actively undermines its value. Students will rightly question why they should attend lectures when AI can interpret, visualize and summarize complex information whenever and however they want, in ways tailored to their readiness level and needs. Grades on essays and exams cannot match the highly personalized (real-time) formative feedback AI tools can offer.

Furthermore, if their value upon graduation lies merely in their ability to recall specialized knowledge, they will soon realize that AI tools can duplicate that skillset at much lower cost and with increased efficiency.

Clearly a new model is needed to supercharge teaching, learning and assessment using insights from the science of learning. One which prepares students to ethically, effectively and critically use, as well as make decisions based on, AI tools and their output. In short, a model which endows them with AI-resilient, “durable skills”.

A skill-based model structures the learning journey around skills mastery, with foundational skills serving as the bedrock upon which more complex skills are built. Such an approach discards lectures for a “flipped-classroom” where skills are independently acquired at home and then applied to real-world problems during class using active learning techniques like Socratic discussion and simulation.

High-stakes exams and essays are similarly replaced with experiential assignments based on real-world challenges. These not only allow students to apply their skills to authentic issues but to be evaluated based on their capacity to iteratively reflect, revise and improve their thinking based on the dynamic challenges they face.

A skills-based model is far better positioned for an AI era than traditional models because it is:

AI tools can – and should – be used by academic leaders, educators and students in a skill-based model. For instance, for:

1. Curriculum design and skills mapping: Academic leaders can use AI to analyse market trends, job descriptions and industry demands to identify the skills they want students to acquire. It can organize those skills into a hierarchical taxonomy and suggest how to sequence skills in order to build a scaffolded curriculum.

2. Content generation: Educators can use AI to generate creative active learning exercises that directly target skills being trained in their course. AI can also help generate ideas for experiential assignments by pulling from databases of current industry challenges or on-going community issues.

3. Adaptive learning and feedback: Educators can use real-time data and feedback from AI to adapt instructional methods on the fly and help automate grading. Elsewhere, students can use those same tools to dynamically adjust the difficulty or focus of learning materials, based on how well they are mastering a skill or concept and to receive continuous, real-time formative feedback based on performance.

4. Performance metrics: AI can help academic leaders monitor the effectiveness of their new skills-based model by tracking key performance indicators, including student engagement, skill acquisition rates and their relation to graduate success metrics. This information can be used to provide feedback on the durability of the skills being trained and direct ongoing curriculum refinement.

The traditional education model, with its focus on teaching students what to think via lectures and exams, was inadequate before AI and appears even more flawed in an AI-integrated world. Worse still, it equips graduates with a skillset that can be easily and cost-effectively replicated by AI.

Educational institutions that embrace the “how to think” model will find themselves cultivating graduates with the “durable skills” that AI lacks, who can harness those same tools to make informed and impactful contributions to society, rather than be sidelined by it.

For an in-depth treatment of the integration of AI in Higher Education, read the full white paper.