A Social Scientist Living the Transition
An undergraduate student (who I have had in a previous class) came up to me after class the other day, “Broberg (students typically call me by my last name), why can’t we have a class of 20 of us who want to spend time with you?” I chuckled and changed the subject. Afterwards, I began thinking about what my life was like being scripted by semester schedules (9:00 AM lecture time), 12:30 PM office hours, a committee meeting at 3PM, and grading until my eyes can’t stay open. Students come in 8 or 16-week blocks. I assess them with multiple-choice tests, essays and discussion board posts that I read over and over again. I lecture. They listen—sometimes.
Then I began wondering what my life could look like with the changing artificial intelligence landscape.
As I envisioned this new life, I saw the traditional version of academia disappearing. Not quietly, and not without resistance. Still, fading. In its place, a radically different model emerged—one where students lead, AI scaffolds, and I, the professor, no longer perform as sage or gatekeeper, but as coach, connector, and co-learner.
Here’s what my typical day looks like now:
8:30 AM – No Lectures. Just Signals.
Instead of prepping a lecture for 200 students in a lecture hall, my AI assistant picks up on a surge of activity in an online forum focused on “neurodiversity and algorithmic bias.” The forum includes a recent high school grad, a retired psychiatrist, a data analyst between jobs, and several students that I know. This is no assigned cohort—it’s a self-organized group formed around shared curiosity: a podcast – a controversial one. Comments are heated and a number of perspectives have been shared. One of my past students asked that I join an online chat – not my traditional way to begin the morning but certainly productive.
Contrast that with the traditional model: rigid course enrollments, syllabus lock-ins, content delivery as the primary act. That model assumed students were empty vessels. This one assumes they are agents in a networked knowledge ecosystem. It’s refreshing to think that my thoughts and actions are valued not for a grade or a required 3-credit hour course.
10:00 AM – A Pop-Up Session Replaces the Lecture
I’m joining a student-organized session on “Post-Work Futures.” It’s in a reconfigured library space. No podium. No rows. Just people in a circle, questions on a shared screen, AI-generated synthesis happening in real time. Here, I’m not the center of gravity. I’m more like a magnetic field—facilitating intellectual movement, not dictating its path. My contribution to the conversation – several questions and a discussion about research projects that are taking place on campus.
In the old model, we feared questions we couldn’t answer. Now, we model what it looks like to sit inside a good question.
11:30 AM – Mentoring Through Portfolios, Not Grades
One of my mentees is preparing for her oral portfolio defense—a structured conversation, not a performance. Her work spans three years of learning: a documentary film, open-source simulations, and collaborative fieldwork with refugee communities. No GPA. No transcript.
We evaluate the portfolio like a thesis committee would—but everyone is a thesis candidate now. The bar hasn’t lowered. It’s risen. It just looks different. No scantrons. No grade curves. Just evidence of thinking, application, and ethical complexity.
The university no longer confers degrees for seat time—it validates mastery through synthesis.
2:00 PM – Grading is Dead. Long Live Evaluation.
In the old system—this is when I’d be drowning in grading—essays skimmed at midnight, hastily annotated rubrics, and the nagging sense that I was part of a machine mistaking compliance for comprehension. Grades functioned as gatekeepers. They sorted, ranked, and excluded under the guise of objectivity. But we knew better. We knew that traditional grading systems often rewarded familiarity with dominant discourse, not depth of understanding. Worse, they amplified inequities. First-generation students, neurodiverse learners, those balancing jobs or caregiving—these students were too often penalized for not fitting the mold. Grading was never neutral. It was a structure of control, built on historical legacies of exclusion and blind to the complexity of lived experience.
Today, I don’t grade. I evaluate—through dialogue, through portfolios, through recursive feedback loops that prioritize growth over performance. A student submits an evolving body of work: a policy analysis rooted in lived experience, a speculative fiction piece exploring algorithmic bias, and reflective memos that document their learning path. There’s no A or B here. There is only the question: Have you made meaning, and can you defend it?
AI assists by surfacing patterns and prompting critical questions—but the heart of evaluation remains human. I engage not as a judge, but as a co-inquirer. Mastery is defended like a thesis, not measured like a multiple-choice test.
This shift also frees up academic labor. Adjuncts are no longer buried in rote grading with little pay and less agency. Instead, they’re positioned as mentors, evaluators, and co-designers of learning pathways. Their expertise matters—because the work of guiding thinking is no longer invisible.
We’ve replaced the fiction of “rigor” with the reality of intellectual integrity. The traditional gradebook is closed. In its place: conversation, complexity, and care.
4:00 PM – Community-Based Research, Not Ivory Tower Detachment
At 4:00 PM, I join a research roundtable—not in a seminar room, but in a shared digital workspace co-hosted with a local nonprofit. They’re working on housing access initiatives and need help making sense of their program’s success metrics. Joining me are five students—though that word now stretches far beyond the 18-to-22-year-old default. One is a formerly unhoused community organizer earning credit through lived-experience documentation. Another is a retiree auditing our discussion on social impact evaluation. A third is a visiting student learning data storytelling through participatory research. All are contributors.
We aren’t “studying” the nonprofit—we’re partnering with them. Together, we interrogate their data: What does success mean to them? How do they measure dignity, not just deliverables? The AI system assists by pulling similar evaluation frameworks from public databases and visualizing community-reported outcomes. We co-design an evaluation plan that is responsive, inclusive, and non-extractive. The nonprofit leaves with actionable tools. The learners leave with experience that can’t be graded but can be defended—and felt.
In the old university, this kind of work was extra—if it happened at all. Now, it’s central. Knowledge doesn’t live in journals or locked LMS systems. It lives in relationships, in reciprocity, and in real-world consequence.
6:00 PM – Dinner and Reflections with AI, Not Rubrics
At day’s end, my AI assistant offers a reflection summary: What questions did I engage? Where did I add value? What did I miss? It tracks not just what I taught—but how I learned. My growth, like everyone else’s, is visible, reviewable, and never “done.”
Epilogue: What We Left Behind—And What We Found
The old university (and the old professor too) was built for a different era: industrial rhythms, scarcity of information, and credentialism as proxy for capability. But the world changed. Information is now abundant, but wisdom is scarce. Credential inflation has diluted trust. And static degrees feel out of sync in a dynamic world. So we let go of class schedules. Of sage-on-the-stage lectures. Of artificial hierarchies between students and teachers. In their place, we built something more alive, more porous, and—dare I say—more human.
Not perfect. But profoundly more honest about how people learn.
And for those of us who were once called professors, we’re not obsolete. We’re just… redefined.
Gregory Broberg, A Future Redefined Professor
Leave a Reply