Educational Technology and Change Journal

By Jim Shimabukuro (assisted by Grok)
Editor

[Also see the reports from Dec 2025, Sept 2025, July 2025]

Several college professors integrate AI into facets of their professional lives beyond the classroom, from accelerating groundbreaking research to streamlining creative workflows and even enhancing personal pursuits. These stories reveal AI not as a distant novelty but as a quiet collaborator that amplifies human ingenuity.

To inspire fellow academics, three such profiles have been selected: Russ Altman at Stanford University, whose blend of caution and curiosity turns AI into a thoughtful ally for discovery and leisure; Alexander Tropsha at the University of North Carolina at Chapel Hill, whose AI-driven quests in drug repurposing echo the thrill of scientific adventure; and Benjamin Stern at Tufts University, whose innovative experiments with AI are reshaping how we predict and prevent human frailty. Each narrative below unfolds like a conversation over coffee, inviting you to imagine slipping AI into your own routines—not to replace your voice, but to amplify it.

The Lens of Possibility: Russ Altman’s Dance with AI at Stanford

In the sun-dappled hills of Palo Alto, where eucalyptus whispers secrets to the wind, Russ B. Altman has spent decades coaxing stories from the invisible architecture of the human body. As a professor of bioengineering, genetics, and medicine at Stanford University, Altman’s days unfold in a labyrinth of labs and lecture halls, where he builds AI technologies not just to mimic life, but to unravel its deepest codes—predicting how drugs dance with proteins, how genes whisper prophecies of disease.

Yet, in the quiet hours after the fluorescent hum fades, Altman isn’t hunched over code alone. He’s tinkering with a new kind of partnership: artificial intelligence as a sparring partner, a mirror that reflects back possibilities he might have overlooked. It’s 2025, and for Altman, who first dipped his toes into AI’s waters back in the 1980s when neural networks were more science fiction than spreadsheet, this tool isn’t a revolution—it’s an evolution, one that enriches the rhythm of his work and the poetry of his weekends.

Picture Altman at his desk, the glow of dual monitors casting shadows like old film reels. A grant proposal looms, its pages a dense thicket of hypotheses and data trails. Rather than wrestle it solo, he turns to an AI chatbot—one his students swear by, a digital oracle he’s subscribed to on a whim. “Summarize this into one page,” he prompts, feeding it the raw draft. The machine hums, spits back a polished abstract, then—unbidden—offers a critique: “Your methodology overlooks ethical variances in patient cohorts; consider expanding here.” It’s a nudge, not a sermon, highlighting angles Altman hadn’t chased in the heat of creation.

He leans back, sips his coffee, and revises—not discarding his voice, but sharpening it. This isn’t laziness; it’s liberation. In a field where funding hangs on the edge of a knife, AI becomes his devil’s advocate, surfacing superficial flaws that spark deeper inquiries. It’s how he chaired Stanford’s AI advisory committee earlier that year, sifting through the tool’s promises for research acceleration against its pitfalls, like widening the chasm between tech-savvy institutions and those scraping by on analog grit. Altman permits AI in his classes too, but with a transparency clause: students must reveal their prompts, like confessing ingredients in a recipe.

Yet he draws lines firmly elsewhere. Letters of recommendation? Those stay pen-to-paper, infused with the idiosyncrasies no algorithm can replicate—the wry observation about a student’s quiet tenacity, the personal anecdote that lingers like a shared laugh. “I want my voice to be mine,” he says, a stance that grounds his optimism.

Because for all its flaws, AI has unlocked doors in his lab: faster simulations of molecular interactions, hypotheses born from patterns too vast for human eyes alone. It’s turned the drudgery of data sifting into a dialogue, freeing Altman to chase the wilder questions—what if we could foresee a drug’s side effects before it ever touches a vein?

But Altman’s embrace of AI isn’t confined to the ivory tower; it spills into the frame of his camera, where light bends like DNA strands. Photography has long been his refuge, a hobby that sharpens his scientist’s eye for the ephemeral. Last spring, eyeing a new lens to capture the fog-kissed Golden Gate at dawn, he hit a wall: reviews piled up like unanswered emails, hundreds of voices clashing in online forums. Enter AI again.

He queried it with specs and budgets, and it distilled the chaos—pros of sharpness in low light, cons of bulk on hikes—cross-referencing user tales with technical specs. What emerged wasn’t just a recommendation, but a revelation: this lens, overlooked in his initial scroll, promised the clarity he’d craved for shots of his kids chasing waves at Half Moon Bay. He bought it, tested it on a misty trail, and watched the world snap into focus, colors vivid as a eureka moment.

That purchase wasn’t just gear; it was a reminder of AI’s gentle power in the personal realm, turning hours of tedium into minutes of insight. No more second-guessing in the darkroom of doubt—now, his hobby hums with efficiency, leaving more space for the joy of the click, the freeze-frame of a life unfolding.

In Altman’s world, AI enriches not by overwhelming, but by undergirding. It amplifies his professional output, letting him author papers with outlines that bloom like well-tended gardens, each draft a collaboration where his edits prune the excess. Personally, it weaves into the fabric of curiosity, from lens hunts to quick summaries of arcane texts that pique his interest mid-hike. He’s wary, of course—the way it might dull students’ critical edges, or entrench divides in academia’s uneven landscape.

But for Altman, at 62, with silver threading his hair like circuit traces, AI feels like an old friend rediscovered: challenging, sometimes clumsy, but ultimately expansive. It hasn’t replaced the thrill of a late-night epiphany scribbled on a napkin, but it has cleared the path to more of them. For colleagues staring at their own stacks of proposals or dusty hobbies, Altman’s story whispers an invitation: dip in, prompt boldly, edit fiercely. In doing so, you might just find the tool doesn’t steal your light—it helps you see farther into your own.

Hypotheses in the Machine: Alexander Tropsha’s Odyssey with AI at UNC

Deep in the Carolina pines, where the air hums with the faint buzz of cicadas and the sharper whir of supercomputers, Alexander Tropsha chases ghosts—the spectral shapes of cures that linger just beyond the horizon of known medicine. As a professor at the UNC Eshelman School of Pharmacy, Tropsha isn’t your archetypal lab-coated alchemist; he’s a computational wizard, wielding equations like incantations to map the unseen pathways of chemicals and diseases.

His office, tucked into the Eshelman Hall’s gleaming corridors, overlooks a campus alive with possibility, much like the AI models he nurtures. In 2025, as headlines scream of AI’s flashy conquests in chatbots and art, Tropsha’s work is quieter, more profound: a multimillion-dollar symphony with ARPA-H funding, where artificial intelligence doesn’t just assist—it hypothesizes, connects, and dreams up salvations for the 78% of human ailments still adrift without treatment.

For Tropsha, a Ukrainian émigré whose accent carries the weight of old-world resolve, AI has transformed research from a solitary grind into a collaborative odyssey, one that pulses with the excitement of lives potentially saved and, in its wake, a renewed sense of purpose that colors his every dawn.

It began, fittingly, with a spark of defiance against the limits of flesh-and-blood intuition. Back in 2018, Tropsha and his team shattered ground with one of the first papers on generative chemical AI—a bold leap where algorithms didn’t merely analyze existing drugs but invented new molecules, tailoring them like bespoke suits to combat elusive pathogens. Fast-forward to this year, and that seed has bloomed into the grandest project of his career: a tapestry of five research groups weaving disparate data threads into knowledge graphs, vast digital webs that link drugs to diseases like constellations in a midnight sky.

At the heart is ROBOKOP, an open-source marvel born from UNC’s Renaissance Computing Institute, a tool that slurps from biomedical oceans—genes, proteins, clinical trials—and spits out answers to the unaskable. “Which genes fuel this rare dystrophy?” it probes. “What off-label drugs might tame them, and what shadows of side effects lurk?” In seconds, it evaluates 2,700 pharmaceuticals against 18,500 maladies, flagging repurposing gold: a blood pressure pill that could quiet a forgotten tropical fever, or an antidepressant repurposed for a genetic orphan disease afflicting one in a million.

Tropsha watches these revelations unfold on his screen, not with detached glee, but with the wide-eyed wonder of a cartographer charting unclaimed lands. This isn’t rote computation; it’s creation, with feedback loops that loop in clinicians to vet predictions, refining the model like a sculptor chiseling marble. For diseases long neglected—those rare beasts that pharmaceutical giants deem unprofitable—AI becomes the underdog’s champion, democratizing discovery in ways Tropsha once only dreamed.

What enriches this for Tropsha isn’t the code’s cold efficiency, but the human warmth it unlocks. At 60-something, with a career spanning continents and crises, he describes this ARPA-H endeavor as “the most exciting and potentially most impactful” of his life—a phrase that slips out mid-interview, laced with the tremor of genuine awe. Imagine the late evenings in his lab, the glow of monitors illuminating stacks of printouts like ancient scrolls.

AI handles the deluge, sifting petabytes where a human team might drown, freeing Tropsha to orchestrate the vision: integrating ROBOKOP’s whispers with wet-lab validations, where synthesized compounds bubble in test tubes under fluorescent skies. It’s halved the time from hypothesis to prototype, turning months of dead-end alleys into weeks of promising trails. In one recent run, the system spotlighted a cluster of antivirals for a neurological scourge, a lead now racing toward trials.

Tropsha pauses over these outputs, not as data points, but as lifelines—reminders that his equations might one day steady a child’s hand or quiet a parent’s vigil. This infusion of speed and scale has reinvigorated his scholarly fire, drawing collaborators from Boston to Berlin, and spilling into mentorships where he urges young chemists to “let the machine ask the questions you fear to voice.”

Beyond the lab’s sterile hum, AI’s touch lingers in subtler ways, weaving professional rigor into personal reflection. Tropsha doesn’t chronicle weekend dalliances with chatbots over chess, but the project’s gravitational pull reshapes his rhythms: early-morning runs through Chapel Hill’s dew-kissed paths, where ideas for graph expansions crystallize like morning mist; family dinners laced with tales of “the AI that thinks like a detective.”

It’s enriched his worldview, too—once a skeptic of tech’s overpromises, he now evangelizes its equity, pushing ROBOKOP’s open-source ethos to empower underfunded labs worldwide. For Tropsha, AI isn’t a crutch; it’s a co-pilot on a voyage toward equity in healing, where the thrill of impact eclipses the tedium of tenure tracks. Colleagues eyeing their own stalled manuscripts or grant rejections might find in his story a gentle prod: invite the algorithm to the table, not to lead, but to illuminate the map. In Tropsha’s hands, AI doesn’t just accelerate science—it rekindles the soul of why we pursue it, turning the abstract into the achingly real.

Predicting the Unseen: Benjamin Stern’s AI Symphony in Physical Therapy at Tufts

Amid the saguaro silhouettes of Phoenix, where desert sun bakes the earth into a canvas of resilience, Benjamin Stern moves like a man attuned to the body’s quiet rebellions—the twinge in a tendon, the falter in a stride that foretells fracture. As an assistant professor in the Doctor of Physical Therapy program at Tufts University School of Medicine’s Phoenix campus, Stern bridges the mechanical poetry of movement with the digital hum of prediction.

His path to academia wound through athletic fields and rehab clinics, from a 2013 NIH-funded whisper of wireless sensors for stroke survivors to collaborations with engineers at UC San Diego and Arizona State University. But in 2025, as AI surges like a summer monsoon, Stern has found in it not a gimmick, but a muse—one that forecasts injuries before they bloom, crafts assessments with the precision of a watchmaker, and even conjures virtual patients who breathe and banter like flesh-and-blood souls.

For Stern, a former collegiate athlete whose own scars from the field fuel his empathy, AI enriches the grind of academia into something symphonic: a harmony of foresight and feedback that saves time, sparks innovation, and restores the human spark to his craft.

It started innocently enough, in the whirlwind of his 2022 arrival at Tufts, when classes swelled to 100 aspiring therapists hungry for guidance. Feedback forms piled like autumn leaves, each demanding hours Stern didn’t have. Enter ChatGPT, that unassuming chatbot, which he poked like a new gadget at a county fair. “Generate questions from this lecture,” he typed, and watched as it spun multiple-choice vines from his notes on biomechanics—queries on gait analysis, laced with clinical twists.

But Stern didn’t stop at raw output; he orchestrated an ensemble of AI agents, digital specialists collaborating in a virtual roundtable. One drafts scenarios: a marathoner with nagging shin splints masking a stress fracture. Another critiques, scanning for the 19 pitfalls of flawed item-writing—bias, ambiguity, overreach—guidelines borrowed from the National Board of Medical Examiners. A third tweaks difficulty, ensuring questions challenge without crushing.

Humans vet the final cut, of course, but the process? It’s slashed creation time from days to hours, birthing robust exams that probe deeper than rote recall. This alchemy secured him a Tufts Data Intensive Studies Center award, embedding these models into the Accelerated Development of Excellence in Physical Therapy program, where they now track student progress like vigilant coaches, flagging strengths in neuro rehab and stumbles in orthopedics.

Yet Stern’s true enchantment lies in research, where AI becomes a seer for the body’s breaking points. Partnering with Arizona State engineers, he co-authored a Nature Communications paper on hybrid machine learning that resurrects incomplete data—like piecing a shattered mosaic from shards. Trained on synthetic time series from flawless systems, the model leaps to infer gaps in real-world feeds: a GPS-tracked sprinter’s stride data, riddled with dropouts from dead batteries or signal ghosts.

Suddenly, patterns emerge—subtle asymmetries heralding ACL tears or ankle sprains—predictions that could sideline non-contact injuries before they sideline careers. It’s an evolution of Stern’s earlier quests: a 2020 Physical Therapy in Sport study on machine learning for athlete risk, now amplified to handle the chaos of sparse sensors.

In his Phoenix lab, amid whirring printers and phantom limb simulators, AI sifts the noise, letting Stern focus on the narrative: how a dancer’s pivot might evade surgery, or a veteran’s limp find fluid grace. Two 2023 grants fueled this, one probing healthcare forecasts, the other educational tweaks, turning abstract algorithms into tangible shields against suffering.

And then, the poetry: AI’s foray into simulation, where Stern summons digital patients as vivid as desert mirages. Drawing from case files, these virtual wards remember dialogues, evolve with feigned ailments—a 20-year-old’s hip ache maturing into arthritic whispers over simulated decades. They arrive with baggage: a harried mom juggling therapy and toddler tantrums, or an immigrant navigating language barriers mid-session.

Students probe via chatbot, practicing empathy in pixels before the raw stakes of clinics. Stern’s blueprint for this, shared in the Journal of Physical Therapy Education, earned him the 2024 Physical Therapy Learning Institute Influencer Award, a nod to how it demystifies AI’s glitches—hallucinated symptoms or stilted replies—into teachable heartbeats. For him, it’s personal alchemy: the tool that once bogged him in busywork now carves space for what matters—mentoring a nervous grad through a breakthrough, or evening jogs where ideas for agent tweaks unfurl like creosote blooms.

In Stern’s life, AI weaves efficiency into epiphany, professional rigor into personal renewal. It hasn’t erased the ache of a long day charting patient arcs, but it has lightened the load, granting hours for family barbecues under star-pricked skies or quiet reads on resilience. At 38, with the energy of a man half his years, Stern sees AI as a bridge to bolder questions: What if we predicted not just falls, but flourishing? For peers mired in grant winters or hobby dust, his tale beckons: experiment, iterate, humanize. In the end, AI doesn’t supplant the healer’s touch—it extends it, turning prediction into prevention, and work into wonder.

__________
Prompt: Have any articles been published in 2025 by or about a college professor using AI in their professional life (not necessarily in teaching), e.g., in research, writing articles, hobbies, entertainment? If yes, please select three that might inspire colleagues to explore AI as a medium for professional development and personal growth. For each, write a 500-to-1000-word essay that reads more like a narrative than a report explaining who they are, where they work, how they use AI, and how it enriches their life. Avoid using bulleted lists. I’m afraid that the scarcity of articles about professors and their personal use of AI might indicate their reluctance to incorporate it into their teaching.

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