Five Ways AI is Challenging the Model of Higher Education

While debate rages over the inappropriate uses of AI, what’s not being talked about enough are the opportunities that next-generation AI tools create for rethinking teaching, learning and assessment at a fundamental level. 

AI and quantum computing — still several years away in terms of large-scale impact — provide a starting point for transforming how colleges and universities support learning.

Five key elements of this transformation, described by EdTech imagineers like Sam Altman (CEO OpenAI)[i]

and change advocates such as Crow[ii], Seldon and Adiboye,[iii] and Winnick[iv] are:

1. An end to “batch” teaching. Instead of working with cohorts of learners in grades or classes, individual learning agendas are developed based on an assessment of the skills, competencies and capabilities the learner already has as well as ongoing conversations about the purpose of the learning. Where does the student want the learning to take them? AI systems, coupled with coaching, mentoring and some instruction and peer support networks then ensure the learner is supported on the carefully mapped learning journey. Teaching can still take place: Students challenged with a set of related issues can be brought together for a boot camp or focused period of instruction before returning to their individualized study activities.
2. An end to “time” as a learning metric. Currently, undergraduate courses are described in terms of credit hours. Learning and assessment on demand, fast-tracking or slow-tracking using adaptive assessment, and modular learning when the student is ready liberates learners from the time-based metric.
3. An end to exams as we know them. Exams are an efficient way to judge the output of batch teaching but are not good indicators of learner knowledge, skills and capabilities. With AI-enabled assessment generation, automated marking and assessment on demand, exams are no longer needed. More authentic assessments that are project-based, team challenges, work-based learning activities, and student self-directed projects can become the norm.
4. Increased feedback and advising. AI systems do not have “office hours.” They are available 24x7 and never tire of finding new ways of responding to student queries and needs.Subject focused chatbots which have been “fed” all necessary information about a subject will do an excellent job of subject-matter advising and teaching.  Analytics suggest that some students need to be supported by teachers or others with special skills, such as writing coaches, statistical support workers and numeracy advisors. The aim will be to ensure every learner follows and is successful in the learning journey they have contracted to complete.
5. Greater accessibility. Higher education systems are organized around blocks of time: semesters or terms. There is no longer good reason for this. Students should be able to begin their learning at any time and be assessed when they are ready. A four-year undergraduate degree could easily be completed in 16 months by a diligent student, for example, or in even less time by someone arriving in the program with substantial life and work experience. AI systems can also leverage technologies to ensure the resources and supports for learners reflect who they are (culture, language, experience and skill) and what challenges they face (disabilities, learning styles, etc.).

This is the kind of “fantasy of the future” behind the educational components of ChatGPT5 and several other AI educational products and services.

These ideas challenge the current business models and processes of education institutions and may lead to the arrival of new entrants to the market. The organizational change and transformation agenda they represent is likely to scare away existing institutions rather than attract them. Nonetheless, the conversation has begun, and these are some of the elements of that conversation.

Notes