Karen Jacobs

Occupational therapy is a profession that promotes a person’s health and wellbeing so they can participate in the everyday occupations—the activities—that matter to them: the things they want to do, need to do, or are expected to do. That’s what drew me to becoming an occupational therapist, and that framing has organized everything I’ve done since.

I still remember watching a child struggle under the weight of an overloaded backpack and asking myself: where is the problem? That question turned into years of scholarship and the National School Backpack Awareness campaign, the longest-running public health initiative in occupational therapy.

Human-centered design is also how I approach innovation. Before I ask what a technology can do, I ask what needs to shift for a person’s participation. The person and their lived experience come first; the technology is evaluated against that, never the other way around. From there, I think about the just-right challenge—enough to stretch someone’s participation, but not so much that it overwhelms it.

I’ve also learned to stay open and curious. Very little of my career was planned years in advance; more often, an opportunity appeared, and I said yes. That’s true of how I’ve approached AI as well: learn about it, ask questions of it, and let what you learn shape where you go next.

Karen Jacobs

I built a structured answer to that question: the TAPESTRY framework, which comprises eight interconnected principles that guide the thoughtful integration of AI in healthcare, education, and the workplace.

• Technology as an enabler
• Authentic human connection
• Person-centered practice
• Ethical guardrails
• Synthesizing evidence
• Therapeutic relationships
• Reflection in action
• Yielding better outcomes.

Each principle represents a thread in the larger tapestry of AI-enhanced practice. When woven together, these threads create a strong, flexible fabric that can support innovation while maintaining professional integrity. In healthcare, and specifically occupational therapy, AI is not a replacement for a practitioner’s expertise but an enabler that augments their capabilities and extends reach. Technology should handle tasks that AI excels at, such as pattern recognition, data analysis, and resource personalization, while freeing practitioners to focus on aspects that require human judgment, empathy, and the therapeutic use of self. The balance is what I call technological mindfulness, borrowing from Schön’s idea of reflection-in-action. I ask practitioners to build in three habits: noticing when technology is shaping an interaction, honestly assessing whether it’s helping or getting in the way, and adjusting in real time based on how the client responds.

Karen Jacobs

AI has made interdisciplinary collaboration even more essential. No single discipline holds the full picture of what responsible AI integration requires: clinicians understand patient impact, engineers understand what these systems can and cannot do, ergonomists and human factors specialists understand usability and cognitive load, and educators understand how to prepare the next generation to work alongside these tools.

The most durable solutions tend to live at the seams between fields, and they only surface when people are willing to sit with colleagues who don’t share their vocabulary and be patient while a shared one takes shape.

That’s precisely the work underway through the International Ergonomics Association’s AI & Work Technical Committee, where professionals from around the world are exploring what responsible and ethical AI at work looks like in practice. Women Applying AI is another example, bringing together women from healthcare, engineering, education, and industry to build AI competency through direct experience. Both remind us that progress depends not only on expertise, but on our willingness to translate across fields, learn from one another, and widen who gets a seat at the table.

Karen Jacobs

The fellowship is what gave rise to Clearing the Air: A Student-Faculty Affinity Group. The idea took shape in a conversation with David Farelo at the Institute, where we found ourselves naming the same underlying problem: faculty and students were each working through their uncertainties about AI in higher education largely on their own, in separate conversations that never quite met. That gap became the premise for the affinity group that honest, ongoing dialogue about AI in higher education must include students as full partners, not simply as recipients of a policy handed down to them.

Working with David, we deliberately opened membership to all BU students and faculty, regardless of discipline or level of AI familiarity. You don’t need to be an AI person to have something important to say about how AI is reshaping the classroom or the experience of learning. The Fall 2026 cohort will meet monthly from September through December. I’m most eager to see what happens when faculty and students sit in the same room, on equal footing, and name what worries and excites them about these tools rather than guessing what the other thinks. My hope is that the norms and insights that emerge will help shape BU’s approach to AI in teaching and learning well beyond this one affinity group.

Karen Jacobs

We followed 72 remote workers for six months using an ecological momentary assessment app paired with Garmin smartwatches. Rather than asking people to reconstruct how their week felt from memory, we were able to capture their experiences as they happened—from comfort and pain to work-life measures and physiological data.

What we found was genuinely nuanced. Younger remote workers with a designated home office and fewer breaks reported the highest life satisfaction, while for many others, pain and discomfort reduced life satisfaction. We also found that flexibility affected people differently depending on their daily routines.

That’s the key translational insight: no single remote-work policy serves everyone well. Organizations need tailored approaches that consider breaks, workspace setup, and flexibility together. Technology-enabled data collection allowed us to see complexity that a single end-of-study survey likely would have missed.

Karen Jacobs

AI belongs in health professions curricula and should be woven through coursework, assignments, and assessment design. In my own courses, I’ve used the Transparency in Learning and Teaching (TILT) framework specifically, so students understand why an assignment is structured the way it is, and what skill I’m trying to build. As for new skills: students need AI literacy in the same way they need statistical literacy; not necessarily knowing how to build the models, but how to critically analyze them. Asking questions such as: Can you tell when an AI-generated clinical summary is missing something important? Can you explain to a patient how a tool contributed to a decision? Can you recognize bias in a dataset or an algorithm’s output? Those are now core clinical reasoning questions that all healthcare professionals need to ask.

Karen Jacobs

I advise starting small, starting transparently, and starting with a real problem you have already identified — not with the technology itself. Don’t ask, “How do I use AI in my course?” Ask “What’s the challenging aspect in my course I’ve been wanting to fix, and could AI actually help with that?” That reframing takes the pressure off and keeps pedagogy the focus. I’d also say: talk to your students about it openly. Give yourself permission to experiment in low-stakes ways before you commit to anything curriculum-wide. Some of the best AI integration ideas I’ve seen came from a faculty member just trying something in one assignment to see what happened.

I encourage faculty to participate in our Clearing the Air Student-Faculty Affinity Group, where faculty and students will build shared norms around AI transparency, trust, and accountability together, rather than faculty writing policy in isolation and sharing it with students.

Karen Jacobs

Curiosity is one of the reasons I’ve been able to stay active after decades of teaching, research, and leadership. I start most days with a small, deliberate habit: asking myself what I might learn today that I didn’t know yesterday. I also read widely, including fiction, because I’ve found that encountering different perspectives supports an innovative mindset.

I think curiosity matters more than ever because technology is moving faster than anyone’s expertise can keep pace. The faculty and students who will thrive are the ones who treat uncertainty as an invitation rather than a threat—who are willing to be beginners again, even at a stage in their careers when they may feel they’re supposed to have it all figured out.

Stay curious and be a lifelong learner, not simply because the field demands it, but because the people you serve deserve it. Build relationships deliberately—with mentors, peers, and especially people outside your own discipline. Some of the most innovative ideas of my career came from sitting at tables where I was the only occupational therapist in the room. So put yourself in those rooms, even when nobody hands you the invitation.