Research Spotlight: Mutual Learning in a Campus Community

We spoke with one of the authors, Annalisa Raymer of Cornell University, to learn more about how CLASP tells its own story, how artificial intelligence was used to provide another perspective on that story, and what adult educators can learn from bringing machine generated analysis into conversation with human judgment.

Adult education is often discussed through programs, outcomes, and credentials, but this article returns attention to the relationships that make learning possible. CLASP brings together undergraduate students, staff members, union leaders, managers, and community learners in a structure that treats every participant as someone who teaches and learns. As the paper states, “Reciprocal relationships are the heart of CLASP.”

For educators and institutional leaders, the article offers a practical reminder that inclusive learning cultures do not emerge only from formal policy. They are also built through repeated encounters in which people listen, mentor, ask questions, adapt, and recognize one another’s knowledge. The article is especially valuable because it shows how student learning, employee learning, labor participation, and university support can be woven into a shared educational ecosystem.

Dr. Raymer reflected on the broader significance of the study: “One implication is both a proof of concept and an invitation to rethink how learning occurs and who is learning. Conference discussants were quite surprised to realize that none of the participating students were education majors, and yet these undergraduates could ‘speak so articulately about adult learning’ and effectively mentor adults while keeping a mindset of mutual growth and development. The learning partnerships of one student and one adult provides opportunities for individuals of different generations to see their partners as fellow knowledgeable, interesting humans with admirable abilities and experiences.”

The article’s contribution is not simply that CLASP received an award, or that an AI tool produced themes from a transcript. Its contribution is the way it places multiple perspectives side by side, the voices in the film, the Evidano analysis, and the responses of expert adult educators at the AAACE conference.

CLASP is presented as a program of mutual learning rather than a one-way service model. The article describes students using andragogical practice to mentor adult learners, while employees bring experience, wisdom, goals, and perspectives that reshape student learning. Nelson’s recollection of the film reinforces this point by emphasizing that the program is about students and employees coming together to learn from each other.

The AI analysis identified themes that aligned with the program’s stated aims. After the film transcript was uploaded into Evidano, the tool summarized perspectives from administrators, union leaders, employees, and students. A second Evidano analysis identified eight prominent themes, including motivation, engagement, language learning and cultural exchange, personal growth and confidence building, mutual learning and personal connection, impact and benefits, and lifelong learning.

Human feedback added context that AI alone could not supply. AAACE conference participants responded positively to the film and did not see adult learners portrayed condescendingly, but they also identified what was unclear or missing. They suggested earlier explanations of learning partnerships, descriptions of the related courses, more history of CLASP, and more attention to the program’s stakeholder composition.

The program’s enthusiastic stakeholders include union leaders, managers, employees, students, human resource officers and community partners. This approach could transform campus cultures more broadly, offering a concrete way for institutions to live their stated values about community, inclusion, and lifelong learning.

The article is a reflective and interpretive account centered on an unfinished CLASP film, the film transcript, Evidano outputs, and audience feedback from the AAACE 2023 annual conference in Lexington, Kentucky. The film included program participants and observers across four roles: administrators and managers, employees and adult learners, union leaders, and students or others taking andragogical courses at Cornell.

The data were complex because they represented many relationships, roles, and forms of learning at once. The authors first used Evidano to summarize the perspectives of each group in the film, then used it again to conduct a thematic analysis of the transcript. They then compared those outputs with comments from adult education colleagues, making the article a qualitative, reflective, and practice-based inquiry into how a program narrates its own educational meaning.

The authors uploaded the film transcript into Evidano and first asked the tool to summarize the perspectives of the four participant groups. They then uploaded the transcript again with an instruction to conduct thematic analysis.

In this workflow, Evidano offered a detached perspective on a program that the authors knew intimately. Its outputs helped identify patterns across participant roles and themes in the transcript, but the article does not treat those outputs as final authority. Instead, the authors placed AI-generated analysis alongside human educator feedback, showing that scholarly judgment, interpretation, and responsibility remained with the authors.

Dr. Raymer also noted how AI tools can support careful research workflows when used responsibly: “Some AI tools are quick to praise and complement the human users, and it is difficult to determine their true utility. Evidano does not do that. With Evidano, I feel that I have a straightforward thinking partner, rather than an internet pet eager to please.”

For researchers, the article offers a balanced example of AI-supported qualitative analysis. Rather than presenting AI as a replacement for researcher expertise, it uses Evidano to create an additional analytic viewpoint. This approach is especially relevant in qualitative and practice-based research, where interpretation depends on context, relationships, and ethical attention to representation.

For educators, CLASP shows how adult learning can challenge assumptions about who holds knowledge in a university. The program asks students to practice facilitation, listening, and adaptation, while supporting employees as adult learners with their own goals and agency. That model speaks to social sciences, education, organizational learning, public engagement, and any field concerned with how institutions build dignity into everyday learning.

For readers considering what to carry forward from the study, Dr. Raymer offered these two takeaways: “One is that principles and practices of adult learning need not be limited to instructors and educators; they are applicable in many fields and settings. Second, the transformative potential of reciprocal, non-hierarchical learning partnerships can transcend traditional institutional boundaries and social divisions while engendering rapport, respect and enjoyment in learning together.”

Taken together, the article reminds us that technology can help surface patterns, but the deeper work of education still depends on human attention, reciprocal respect, and the courage to learn across difference.