TENNAIR 2026

The Impact Credential Model is the result of SCORE’s effort to align Tennessee around a collaborative vision for the educational credentials we want every Tennessean to attain. Through building an analytical model (twice!), SCORE encountered challenges demanding innovative solutions.  In this session, the audience will learn what the Impact Credential Model is, how SCORE translated ambiguous problems to data-driven answers, synthesized diverse external data sources, used a directed acyclic graph (DAG) to map data flow, collaboratively wrote code using Python and Git, and elevated the impact credential model in statewide conversations. Participants will additionally learn how we made mistakes and how we seek to improve the credential impact model in the future.

As higher education institutions invest in artificial intelligence, Institutional Research (IR) offices are uniquely positioned to translate these capabilities into meaningful, trusted insights. However, AI adoption is only as effective as the data foundations that support it.
This session explores how data modernization efforts—standardized definitions, governed data pipelines, and scalable reporting structures—enable practical and responsible AI use within IR. Rather than focusing on theory, the session highlights concrete use cases where AI can enhance IR functions, including reporting automation, enrollment analysis, and workforce alignment.
Presenters will also examine the evolving role of IR in an AI-enabled environment, with particular attention to governance, data quality, and maintaining institutional trust. Attendees will leave with a pragmatic framework for identifying AI opportunities, avoiding common pitfalls, and positioning their IR office as a leader in data-informed decision-making.

Institutional Research (IR) professionals often seek to go beyond simply providing data to contribute more to analysis, consultation, and strategic decision-making. However, their ability to do so is shaped not only by what they produce, but by how their work is perceived across campus. When IR is primarily understood as a reporting or compliance function, opportunities to engage more deeply in interpretation and strategy may be limited, regardless of an office’s capacity or intent. This challenge can be especially pronounced during institutional transitions, when leadership turnover, shifting responsibilities, and gaps in institutional knowledge complicate the role of IR.

This session presents a case study of a qualitative approach with theory-informed analysis used to better understand the role and perception of an IR office at a mid-sized private university. The presenter entered the role as a first-time IR leader following a multi-year vacancy in the position, during a period marked by significant turnover among campus leaders and staff who regularly interact with IR. As a result, both the office's identity and its relationships across campus were unclear.

To better understand this context, the presenter conducted a series of structured listening sessions with administrative leaders across campus. Interview data were analyzed using an integrated framework that combines stages of IR office development (data supplier, analyst, consultant, leader) and institutional logics (market, state, and profession) (Brown et al, 2017; Brown, 2017). This approach enabled a more nuanced understanding of how different stakeholders interpret IR work, how misalignment in institutional logics may shape perceptions of value and expertise, and potential strategies for increasing IR's influence at the institution.

Rather than prescribing a single model for IR practice, this session offers a structured approach to conducting listening sessions, analyzing qualitative data using theoretical frameworks, and identifying patterns in institutional perceptions. Participants will be encouraged to reflect on how their own offices may be perceived and will leave with practical strategies for using qualitative inquiry to better understand and navigate their institutional context, particularly in periods of transition or ambiguity.