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Learning in contemporary higher education is rooted deeply in calendars and time rather than mastery of the topic of the learning. With an inflexible semester or quarter calendar and an often-inflexible schedule and length of meeting times, learners are marched through the system in the orderly method of an assembly line.

As long as I have taught at the university level, beginning in the early 1970’s, I have questioned this approach that puts time scheduling ahead of depth of learning. It seems to put teaching schedules ahead of learning outcomes. I must confess that over the decades, I have been an “easy touch” for an incomplete for a student who encountered some unforeseen life disruption or simply took on more than she or he could handle during the semester. My philosophy has been, and still is, that what is learned is more important than whether it was accomplished in 8 weeks, 16 weeks, or even longer.

I am not alone in that view. Back in the 1960’s one of learning’s highly recognized scholars, Benjamin Bloom, probed this very issue. “Bloom’s Learning for Mastery (LFM) strategy evolved and was later on implemented in primary and secondary school settings.” Meanwhile psychologist Fred Keller developed his Personalized System of Instruction (PSI) focused on five key principles:

1. 1. Students should be allowed to work at their own pace.
   2. Students should achieve at least 90% accuracy on the assessment before moving to the next lesson.
   3. Lessons should be considered as ‘vehicles of motivation.’
   4. Teachers and students should consider using written communication in textbooks and study guides.
   5. Teachers and students should get closer through repeated testing, immediate scoring, continuous tutoring, and progress tracking.

These five principles cannot be easily integrated into classes that march forward with a rigid class calendar based on three 50-minute class meetings for 15 weeks! However, recent technological developments have opened the door to reinventing higher education from the assembly-line model to an online-asynchronous, tutor-enhanced, mastery-based learning model.

To understand the differences between traditional teaching and mastery learning, one can best describe that our current practices place an emphasis on time-based teaching, while in mastery learning, the emphasis is, as the name suggests, on learning. Note that in Keller’s PSI approach, the goal is 90% or better learning as evidenced through frequent assessments required to move forward to the next module in the class. The PSI stresses personalized scaffolding of learning and evidence of mastery throughout the course. Whereas our current common mode of delivery views the class as a whole rather than recognizing differences in background and learning by individuals. Inevitably, the current approach penalizes students for unintended, unrecognized shortcomings in understanding caused by any of a number of circumstances such as prior knowledge deficits in some aspect of that which they are studying; poorly taught pre-requisite or assumed previously taken classes; unanticipated life interventions; or some other inability to learn essential class concepts that had not been anticipated in the development and design of the class. If, instead, we were to create personalized learning intervention opportunities at every step of the way that are designed to be responsive to the needs of individual learners on a minimum of learning 90% of every module, we could ensure a minimum of mastery of 90% of the materials in every class.

Artificial intelligence employed in an asynchronous or blended online class opens the pathway to mastery learning. An instructor can experiment with this process by folding this prompt into one or two modules of a class. Released by “There’s an AI For That” (TAAFT), is a free and openly available prompt that can be inserted into any of the major frontier models such as Gemini, ChatGPT, or Claude. Titled Precision Learning Companion, the prompt is introduced:

This prompt turns AI into an ultra-detailed, dynamic personal tutor that doesn’t just quiz, it teaches deeply, layer by layer, until the user genuinely masters the material. It’s built to adapt in real time, constantly diagnosing knowledge gaps, and never moving forward without full comprehension. Every answer, right or wrong, triggers a structured, narrative-style breakdown explaining the what, why, how, and broader context, ensuring true understanding. The AI is designed to feel like having a supportive but meticulous mentor who scaffolds learning: progressively challenging the user if they perform well, slowing down and simplifying if they struggle, and always reinforcing psychological safety through encouragement. It uses textually described visual aids, memory tricks, real-world examples, and step-by-step remediation when needed. Mastery, not speed, is the goal.

I encourage readers to test this out to examine more closely the impact of using AI to deliver learning opportunities. It literally took me less than two minutes to get my module up and running:

1. 1. I copied and pasted the page-long prompt from the TAAFT.Notion site into Claude 4 for test purposes and pressed enter
   2. I entered the topic as “human eye anatomy” (of course you can enter any topic that you might cover in a week or so in your current class schedule)
   3. I was then launched into a congenial conversation with the AI module that probed deeply into my knowledge of the topic in a pleasant and reinforcing way.
   4. I must admit that I was so engaged that I didn’t stop for more than an hour.

You can begin by testing it on yourself and perhaps a colleague, teaching assistant, or another willing participant. Choose a relevant topic. I chose “physiology of the human eye” which was a basic module in all of the many Communication Technology classes I offered.  I found the AI module to be accurate, comprehensive, reinforcing and clear. If you find that it shows promise, you might choose to use it in one of your classes. Invite your instructional designer to join in a discussion of how this might best be used in your classes. Note how it personalizes instruction for learners by sharing additional information, readings, and related learning opportunities to backfill areas that learners who may be deficient in background and need context to relate to the course. You can ask learners to share a copy of the exchanges. They may also share brief reactions on the quality and usefulness of the interaction with AI.

Over time, with the help of your instructional designer, you may want to go fully into mastery learning, ensuring that every student in your classes masters the material at a 90% level. In some cases you may need to be flexible with offering “Incompletes” to provide time for those who need to complete the additional material triggered by submission of wrong answers. 

I always had an uncomfortable feeling in the pit of my stomach when I submitted a C, D, or F as a final grade. I felt that I had failed my student. However, I had a full classroom and there was not enough time or opportunity to provide individualized attention to each student. Perhaps the new generation of university instructors who partner with AI assistants, will enjoy the confidence that all of their learners will master the topic of the class with the help of AI. No learner will be left behind, and none will be victims of the assembly line model of teaching in higher education.

 

This column was originally published in Inside Higher Ed.