EMERGING DIALOGUES IN ASSESSMENT

An Overlooked Dimension of Generative AI in Assessment:
Faculty Emotion and Professional Identity

Most conversations about generative AI in higher education focus on students: how they are using AI, how institutions should regulate it, and how instruction and assessment should change in response. Faculty typically appear in these discussions only secondarily, as the people who must update syllabi, redesign assessments, and learn new tools. Yet faculty are also living through this transition. Their responses involve more than knowledge or compliance; they also involve emotion, professional identity, and how faculty interpret what AI means for their roles. If higher education wants to respond effectively to AI-related change, these dimensions deserve more attention.

This matters for assessment practitioners because faculty responses to AI shape how institutional policies, instructional changes, and assessment reforms are interpreted and enacted. If assessment leaders want to understand mixed results of AI initiatives, they should look beyond measures of faculty knowledge and use. They should also attend to how faculty experience this shift in terms of their roles, capabilities, and perceived opportunities.

One useful way to think about faculty adoption and implementation is through three related dimensions: identity disruption, adaptive agency, and opportunity orientation. Together, these dimensions describe how faculty interpret AI-related change in terms of professional identity, perceived capability, and perceived opportunity. This framework provides a way to understand responses that might otherwise be reduced to simple categories such as levels of support, resistance, or AI literacy.

Identity disruption refers to the extent to which generative AI unsettles a faculty member's sense of professional role, expertise, or stability. Research on professional identity emphasizes that work roles are closely tied to meaning, competence, and self-concept (Ashforth et al., 2008; Beijaard et al., 2004). For some faculty, AI may feel like another tool. For others, it raises a harder question. A scholar who has spent a career developing disciplinary and pedagogical expertise, such as teaching writing or other complex intellectual skills, is not facing a question about tool adoption but about whether a core professional competence tied to identity is becoming devalued. When AI appears to threaten hard-won expertise or destabilize traditional academic roles, responses may reflect a deeper sense of professional disruption rather than simple skepticism about a technology.

Adaptive agency refers to the extent to which faculty feel capable of responding effectively to AI-related change. This idea draws on work in self-efficacy and occupational efficacy, which suggests that perceived capability influences how people interpret and respond to difficult conditions (Schyns & von Collani, 2002; Tschannen-Moran & Woolfolk Hoy, 2001). In this context, adaptive agency includes whether faculty feel they have the strategies, control, and capacity to adjust their teaching and scholarly practices without losing the integrity of their work. Some may feel concerned but capable. Others may feel uncertain or overwhelmed. Distinguishing adaptive agency from general attitude helps assessment leaders understand why similar institutional conditions can produce very different responses.

Opportunity orientation refers to the extent to which faculty perceive meaningful upside in the emerging academic environment. Research on the meaning of work suggests that work can be experienced in positive, growth- and purpose-oriented terms, not only as obligation, stress, or loss (Steger et al., 2012). Research on technology readiness makes a complementary point about openness to new tools (Parasuraman, 2000). Some faculty may see generative AI as creating new possibilities for teaching or scholarship. Others may see little benefit at all. Including this dimension matters because faculty responses are not uniformly positive or negative. A useful framework should be able to account for creative exploration and constructive adoption, not only for anxiety or resistance.

Faculty responses to AI involve more than technological competencies. Institutions often rely on anecdotal impressions when interpreting how faculty are navigating AI-related change. A faculty member who resists an AI initiative may be experiencing genuine identity threat, not a knowledge deficit. A faculty member who adopts AI tools enthusiastically may still feel low agency about the longer-term trajectory of their discipline. Without a more structured way to examine these responses, assessment leaders risk misreading the landscape.

One practical implication is that institutions could begin examining these dimensions more explicitly. A short set of targeted survey questions could help assessment leaders understand how faculty are navigating these changes across roles, disciplines, and local contexts. Assessment offices that are already surveying faculty about AI, teaching, or assessment climate could add one item per dimension, drawing on the example faculty survey items above. Such data could help improve interpretations of mixed implementation results, identify key areas where faculty assistance is needed, and inform more thoughtful institutional planning. Even before a fully validated instrument is available, this framework could help campuses ask better questions.

Including a few targeted questions about professional disruption, perceived capability, and perceived opportunity would give assessment offices a more complete picture of how AI-related change is being experienced on campus and why institutional initiatives are received differently across individuals and disciplines.

Assessment conversations about generative AI will remain incomplete if they focus only on students, tools, and policies. Faculty are also living through this disruptive transition. By examining identity disruption, adaptive agency, and opportunity orientation, assessment leaders can gain a clearer understanding of how faculty are interpreting and responding to AI-related change. Including these dimensions would give institutions a more effective foundation for responding to AI as it continues to reshape higher education.

REFERENCES

Ashforth, B. E., Harrison, S. H., & Corley, K. G. (2008). Identification in organizations: An examination of four fundamental questions. Journal of Management, 34(3), 325–374. https://doi.org/10.1177/0149206308316059

Beijaard, D., Meijer, P. C., & Verloop, N. (2004). Reconsidering research on teachers' professional identity. Teaching and Teacher Education, 20(2), 107–128. https://doi.org/10.1016/j.tate.2003.07.001

Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001

Schyns, B., & von Collani, G. (2002). A new occupational self-efficacy scale and its relation to personality constructs and organizational variables. European Journal of Work and Organizational Psychology, 11(2), 219–241. https://doi.org/10.1080/13594320244000148

Steger, M. F., Dik, B. J., & Duffy, R. D. (2012). Measuring meaningful work: The Work and Meaning Inventory (WAMI). Journal of Career Assessment, 20(3), 322–337. https://doi.org/10.1177/1069072711436160

Tschannen-Moran, M., & Woolfolk Hoy, A. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783–805. https://doi.org/10.1016/S0742-051X(01)00036-1