While AI is being used to make self-tests, study aids, and a wide range of other learning materials, tutoring systems have arguably the most potential to improve student understanding and performance. Programmed to mimic human tutors, these systems engage in a Socratic dialogue with the
AI has opened a world of new opportunities for student learning. I have discussed how instructors can easily add interactions to their learning material, tutors to their classroom, and digital laboratory simulations that allow students to test how a process changes with different inputs. But
During a recent interview for a longitudinal study on undergraduate writing and learning that I am conducting at my school, one student described a course he was currently taking with a professor he admired deeply. He spoke warmly about the instructor’s passion, clarity, and care
Large language models can explain concepts, summarize readings, and even generate practice problems. But developing professional expertise—the kind of strategic thinking, judgment, and problem-solving that distinguishes novices from experts—requires more than content delivery. It requires coaching. When we wanted to give students more access to
Faculty and institutions are using AI to create educational material and tutors that help students learn the material. These are text-based interactions. But AI can also build digital simulations that allow students to experiment with different variables to learn how they affect processes. This ability
Every teacher knows the challenge: Students complete homework at 9:00 p.m., get stuck, and have to wait until the next day’s class for help. By then, you’re facing a mountain of assignments that all show the same misconception.
Instructors and students waste many hours struggling to get what they want out of an AI chatbot due to a lack of understanding of prompt engineering. With AI playing an increasingly important role in work, prompt engineering is one of the most important skills our
Students often struggle to understand complex or abstract concepts, especially when they cannot see how those ideas connect to their lives. I’ve learned that if students cannot see themselves in a concept, they usually cannot fully grasp it. Case studies can help by placing course
Imagine that all AI applications are surrounded by a high, impenetrable fence. At the gate stands a calm but firm Gatekeeper who unlocks the gate only when a student has an acceptable reason to use AI for a specific task.
Most people think of AI only in terms of answering questions or creating works such as images and songs. But AI’s power lies in its ability to interact with users, and to this end, instructors can use it for highly realistic chat-based simulations without the
