Editor’s note: This article first appeared on Alan Lesgold’s Medium.com page and is reprinted with permission.
By ALAN LESGOLD, CYNTHIA GOLDEN and MICHAEL BRIDGES
In a recent article in the Chronicle of Higher Education, Paul Schofield argued that college centers for teaching and learning (CTLs) are often problematic, characterizing them as administrative bloat staffed by nonfaculty who erode course quality by substituting generic pedagogy for discipline-specific wisdom. Bryan Alexander recently published a robust response in the Chronicle, rightly pointing out the flaws in Schofield’s evidence and the “cartoonish” binary he draws between interfering staff and virtuous faculty.
While Alexander provides a necessary and thoughtful rhetorical defense of CTLs and highlights important aspects of their value, the conversation must move beyond Schofield’s binary choice to keep or close a CTL. The real issue is not whether centers that support instructional design are necessary, but rather whether they work as productive partners with disciplinary faculty rather than transactional course-production shops.
More importantly, as higher education faces the existential challenge of artificial intelligence, CTLs also must become applied research and development hubs focused on making college teaching and learning more successful in a time when more adult education is needed and budgets are tightening.
Effective teaching requires the integration of clear content goals with optimal course design, appropriate use of technology, classroom management skills, and well-designed learning spaces. Done well, a center for teaching and learning is a critical asset for a university. Partnering with disciplinary faculty, it provides a foundation for robust diagnosis of barriers to learning, solving instructional problems, and shaping the environment in which teaching happens.
Colleges are complex systems. If we were professors at a small college facing economic pressure, we too might feel that funds given to teaching centers would be better spent retaining faculty. Moreover, as a former education dean and former teaching and learning center directors, we have seen striking examples of problematic generalist approaches.
Indeed, during his tenure as dean, one of us closed an instructional design program precisely because it taught future education specialists to impose generic approaches on courses, regardless of their specific purposes. Schofield criticized this transactional model. However, based on decades of experience, we believe that the partnership model of CTLs contributes substantially to improving the learning opportunities departments offer their students.
Effective course design starts from principles, not templates
The best teaching centers do not begin with general tools or teaching approaches and try to force specific courses to fit them. Rather, they start from core principles of learning that have a strong evidentiary basis and are basic human properties present in students regardless of what content they are trying to master. They ask challenging questions about how those principles might apply to the task of helping students develop specific knowledge and competence. Five such principles are:
Expectancy and value matter: Students are motivated when they believe they can succeed (expectancy) and believe the task is worth their effort (value).
Cognitive capacity is limited: Storing or processing information outside the brain — in tools, artifacts, or the environment — frees up limited cognitive capacity for essential thinking.
Learning often benefits from collaboration: Learning can be enhanced by a structured approach to group thinking, in which learners construct collective understanding through evidence-based argumentation.
Metacognition is essential to deep learning and performance: The capacity to monitor, evaluate, and regulate one’s cognitive processes during learning makes the learning process more effective.
Learning benefits from productive struggle: Rooted in Vygotsky’s Zone of Proximal Development and Kapur’s work on “desirable difficulties,” productive struggle or effortful sense-making builds deeper understanding.
While there are other learning principles, these five exemplify what drives a teaching center’s dialogue with a professor. This dialogue — not the shoehorning of a syllabus into a particular teaching approach — is the hallmark of the best teaching centers. Because teaching and learning are complex, courses benefit from critical interactions in which a teaching specialist reminds a professor of how learning works, while the professor ensures that their specific course goals are addressed.
In the University of Pittsburgh Teaching Center, where we have experience, this dialogue is enriched by the diverse prior teaching experiences of our CTL colleagues in subjects such as physics, law, education and statistics. Most hold doctorates and have taught a diverse range of subjects in addition to acquiring mastery of the learning sciences. They are effective because they champion key learning principles while understanding that specific learning goals require tailored approaches grounded in the scholarly traditions of different disciplines. Such cross-disciplinary collaborations are standard in research; they also enhance teaching.
Centers for teaching and learning are also critical to the pedagogical development of new instructors. They offer training to the teaching assistants and adjunct instructors on which larger universities depend, as well as to new faculty who may have little or no prior teaching experience. Even many experienced instructors are not familiar with the science of teaching and learning. Frequently, departmental discussions of teaching are rooted in personal strategies (e.g., this works for me), with limited understanding of why they work. Teaching centers bridge the gap between theory and practice. In the best partnerships, faculty assure the adequacy of curriculum while CTL consultants help assure that courses achieve their curricular goals.
The AI Imperative: CTLs as applied R&D hubs
Now, though, centers for teaching and learning need to do more. In the exponentially advancing age of artificial intelligence, CTLs must become applied R&D leaders in developing ways to use new tools to make learning opportunities more effective.
Research in higher education typically runs on a multi-year cycle: hatching an idea, conducting experimental trials, analyzing data, and undergoing editorial review and publication. While this process works well for understanding stable phenomena, it is far too slow to ensure effective and economical learning opportunities during periods of rapid change. The very argument against generic, one-size-fits-all teaching prescriptions is also an argument for the rapid prototyping and testing of new teaching approaches as powerful technologies emerge. Teaching centers should be at the forefront of this rapid experimentation, gathering data on which approaches prove useful for specific disciplinary teaching purposes.
This is particularly true for AI tools. While generic prompts may be useful in some situations, different disciplines with varying epistemological approaches likely require AI tools tailored to their specific methods of developing, testing, challenging, and applying knowledge. In any university, enterprising faculty will want to try new tools and approaches. A teaching center can support these trials and, crucially, help gather, curate, and share what individual faculty learn about what works, when, and for whom.
The assessment crisis
One key need for innovation and rapid prototyping is improved assessment of learning. Historically, and especially in large-enrollment courses that require careful management of faculty grading time, assessment has used predictive measures: multiple-choice and short-answer questions and small performances such as relatively short essays. With the emergence of powerful AI tools (that students are mastering faster than faculty), AI-enhanced cramming has made multiple-choice testing and short-answer testing less likely to predict true content mastery and AI also has been used by some students to produce essays not based on their personal mastery of a subject.
Centers for teaching and learning need to foster experiments with new assessment approaches such as AI-facilitated oral examinations and “stealth assessment” that tracks the steps a student is taking in solving a complex problem and infers what they know from the details of their performance. We have not had the faculty resources to extend oral examination from the graduate level down to large courses, but we may be able to find ways for AI to help us do that.
We similarly lack the resources to deeply examine the steps a student takes while solving a complex problem, but again AI tools may allow us to do that. As teaching and learning evolve, emerging technologies could help us replace outdated assessments with more thorough and direct evaluations of student knowledge and their ability to apply what they’ve learned.
Helping faculty master new technologies
While corners of a university may still resemble Aristotle’s academy, many societal learning needs are met in more complex ways. Online, hybrid and high-enrollment courses have made course design more complex. The idea that a single faculty member should fully master content, pedagogy, technology, accessibility and assessment is increasingly unrealistic. Instructional design partners are needed because the landscape has changed.
New AI tools and studies detailing their capabilities appear weekly. It is exceedingly difficult for any faculty member to stay abreast of this exponentially growing knowledge base. Faculty cannot learn and evaluate every potentially useful new technology while maintaining substantial teaching and research agendas. They need just-in-time delivery of specific AI knowledge that helps them accomplish their specific teaching goals — essentially, personalized learning opportunities about AI.
Teaching centers are the obvious place to provide this necessary updating. To do so effectively, they must create hybrid arrangements that use both AI tools and expert human judgment. This approach can efficiently develop just enough training to allow interested faculty to use emerging technologies in high-payoff ways, improving student learning with a reasonable investment of faculty time. While university IT departments can provide links to generic courses on specific AI tools, teaching centers are best positioned to offer the course-specific, just-in-time knowledge that enables faculty to try innovative approaches.
Summary
At least in larger institutions, there is both a clear need and a defined role for teaching centers. To fulfill this role, they must view themselves as partners with faculty in a critical dialogue about how learning happens, how specific learning within specific disciplines can best be supported, and how it can reliably be assessed.
Artificial intelligence is a rapidly growing technology subject to Wright’s Law; it will continue to become cheaper and better at an accelerating rate. Increasingly, the private sector is moving into professional education spaces traditionally occupied by colleges. As for-profit entities, these companies focus on educational programs that can run in the black, and they are poised to market new learning opportunities needed by professionals who are finding some of their roles taken over by AI.
Teaching centers, when properly staffed and configured, can help ensure that universities stay competitive in offering learning opportunities that align with their missions. While not every existing center is up to this task, many are, making them extremely valuable assets to higher education’s future.
About the authors
Alan Lesgold is dean emeritus of the Pitt School of Education and professor emeritus of education, psychology, and intelligent systems. He is a volunteer advisor for Pitt’s Center for Teaching and Learning. Cynthia Golden is a former associate provost and executive director of Pitt’s Center for Teaching and Learning and a consultant with Vantage Technology Consulting Group. Michael Bridges is also a former executive director of Pitt’s Center for Teaching and Learning and continues to teach part time at Carnegie Mellon University.