What MIT Can Learn from Israel’s Public Colleges About AI
As universities rethink teaching and learning in the age of artificial intelligence, Israel’s public colleges have valuable experience to contribute.
When I read MIT’s recent report on artificial intelligence in teaching and learning, I was struck by how familiar many of its recommendations sounded.
Define what students should know and be able to do. Align assessments with learning goals. Use real-world problems and professional experience to deepen learning. Support faculty as they rethink how they teach.
These are important recommendations, but they are not new, nor are they unique to AI. They reflect principles of good teaching that have guided educational change at Jerusalem Multidisciplinary College (formerly Hadassah Academic College) for more than 15 years.
Our responsibility as educators is not simply to help students earn degrees. It is to prepare them to enter the workforce and remain effective throughout their careers. We must ask what graduates need to know, what they should be able to do, and how they will continue learning as their professions evolve.
Israel’s Council for Higher Education and its Planning and Budgeting Committee have reinforced this approach through Academia 360, launched in 2024 to encourage institutions to update undergraduate programs and strengthen their relevance to a changing labor market.
MIT deserves credit for examining these questions in the context of AI. The technology can help students explore ideas and undertake ambitious projects, but it can also allow them to complete assignments without developing the understanding those assignments were designed to build.
Israel’s public colleges have valuable experience in addressing precisely this challenge.
Learning to Think, Not Just to Remember
About ten years ago, a lecturer teaching ocular pathology at our college told me, with considerable pride, that her students planned to print her PowerPoint presentations and keep them as a reference guide for their future clinical work.
I understood why she was pleased, but I was concerned.
In healthcare, as in many professions, knowledge changes constantly. Diagnostic methods evolve, new treatments emerge, and accepted practices are revised. A set of lecture slides, however excellent, cannot serve as a professional reference for an entire career.
Students need foundational knowledge, but they must also learn how to find current information, evaluate evidence, and apply what they learn to unfamiliar situations.
Following our conversation, the lecturer redesigned her teaching around real clinical cases. Students investigated diagnostic and treatment options, presented their reasoning, and answered questions. They also treated patients in our teaching clinics and wrote case reports connecting scientific knowledge with clinical findings.
Their learning became visible not simply through what they remembered, but through how they reasoned, made decisions, and applied knowledge to patient care.
This change was part of a broader transformation at our college. More than 15 years ago, a European Union Tempus grant helped us introduce student-centered learning in our Optometry and Communication Disorders departments. We began designing courses around what graduates needed to know and be able to do, rather than simply what lecturers wanted to teach.
A subsequent Erasmus+ project helped us rethink assessment. We introduced greater emphasis on formative assessment, using frequent, low-stakes assignments and feedback to help students identify gaps in understanding and improve throughout a course.
Over time, these approaches became college-wide policy.
These changes were not driven by AI. They were driven by the recognition that preparing students for professional life requires much more than transferring information.
AI now makes that recognition even more important.
If AI Can Do the Assignment, What Are We Assessing?
An assignment that once demonstrated a student’s understanding may now be completed almost entirely by an AI tool.
The solution is not necessarily to prohibit AI, nor is it to introduce AI into every course. Instead, we must reconsider which abilities students should demonstrate independently, where AI can enhance their work, and how we can assess the reasoning behind what they submit.
Our Laboratory Sciences program offers a practical example.
Students may use AI to help prepare presentations about scientific papers. But they must submit the original articles, present the research orally, and answer questions.
Can they explain the methods? Do they understand the limitations? Can they interpret the findings?
Their answers tell us far more about their understanding than the quality of their slides.
AI can support the preparation. The student remains responsible for understanding the science.
MIT’s report makes a similar distinction. Rather than abandoning examinations or introducing AI everywhere, it recommends deliberate choices based on learning objectives, including oral examinations, portfolios, and classroom discussions.
The question is not simply whether students used AI. It is what they learned and whether they can demonstrate that learning.
Preparing Graduates for a Changing Workplace
Public colleges have particular experience in connecting academic learning with professional practice.
In healthcare, social work, education, engineering, and management, students must do more than acquire knowledge. They must apply it, exercise judgment, solve problems, and communicate effectively.
At JMC, our internship program, supported by the Council for Higher Education, gives students academic credit for professional experience. An accompanying course helps them connect workplace experience with academic learning and reflect on the competencies they are developing.
These opportunities will become even more valuable as AI transforms professional work.
AI may perform tasks that graduates once spent years learning to do. But professionals must still evaluate its results, recognize errors, and take responsibility for their decisions.
We need to distinguish between abilities students must master independently and those in which AI can extend their capabilities.
Making these changes requires more than purchasing technology. At JMC, our Center for Teaching Excellence and network of AI champions help faculty experiment, redesign assignments, and share effective approaches. The real investment is in people and pedagogy, not simply software.
AI Must Expand Opportunity, Not Inequality
There is another reason public colleges should help shape this discussion: their commitment to expanding access to higher education.
Many of our students are the first in their families to attend college. They come from diverse communities and enter higher education with different levels of preparation and financial resources.
AI could help reduce some of these differences. It can provide individualized explanations, feedback, language support, and learning assistance that might otherwise be unavailable.
But it could also widen existing inequalities.
Students who can afford advanced AI subscriptions may gain advantages over those relying on free tools. Students with stronger digital skills may benefit more than those with limited technological experience.
MIT’s report raises similar concerns about unequal access to AI resources.
If we expect students to use AI in their academic work, we have a responsibility to ensure they have meaningful access to appropriate tools and the training needed to use them effectively.
Equity is not simply about giving students the same software. It is about ensuring that students from different backgrounds have a fair opportunity to benefit from the technology.
We Have Experience to Share—and Much More to Learn
One of MIT’s guiding principles is particularly relevant: “Be humble.”
Our experience does not mean we have solved the challenges AI presents. The technology is evolving rapidly, and we are still learning how it affects students’ understanding, independence, and professional development.
But we are not starting from zero.
For more than a decade, Israel’s public colleges have been developing approaches that emphasize learning outcomes, professional competencies, practical experience, and continuous assessment. These provide a valuable foundation for responding to AI.
Israel should build on this experience and the investment already underway through Academia 360, supporting faculty development, assessment redesign, evaluation of new teaching approaches, and equitable access to AI tools.
Above all, we must remember our central mission: preparing graduates with sound professional knowledge, independent judgment, and the ability to adapt and continue learning throughout their careers.
AI has changed the tools available to us. It has not changed that responsibility.
Disclosure: I used AI in preparing this article. I wrote the original draft and asked ChatGPT for editorial feedback. I reviewed and incorporated some of its suggestions into the final version.
