The 2026 Information Technology in Academic Medicine Conference, sponsored by the AAMC Group on Information Resources (GIR), brought together technology, data, education, and operational leaders to explore how academic medicine can move from experimentation to sustainable, scalable, and responsible technology adoption.
Held in Austin, Texas, this year’s conference theme, “Blazing a Trail for Solutions,” reflected the practical tone of the meeting. Across plenaries, breakout sessions, hot topic discussions, and posters, the focus was not simply on what new technologies can do, but on what institutions need in order to use them well.
Several themes emerged throughout the conference, including the growing role of AI in education and research, the importance of data governance, the operational realities of digital transformation, and the need to align technology decisions with learner support, institutional strategy, and long-term sustainability.
From AI Exploration to Responsible Implementation
Unsurprisingly, artificial intelligence was one of the dominant threads running through the conference. The most useful conversations moved beyond general excitement and toward implementation, governance, institutional readiness, and measurable value.
Sessions explored AI across clinical research, medical education, instructional design, media production, simulation, curriculum support, and operational workflows. Across these conversations, the common thread was that applying AI to academic medicine cannot be treated as a purely technical exercise. It requires thoughtful planning around privacy, bias, transparency, consent, evaluation, cost, and long-term support.
This is especially important in educational contexts, where AI tools may influence how learners access content, receive feedback, practice communication skills, prepare for clinical reasoning, and interact with academic support systems. The challenge is not simply whether these tools can be deployed, but whether they can be deployed in ways that are educationally sound, ethically responsible, and operationally sustainable.
AI Readiness Requires More Than AI Enthusiasm
The closing plenary focused in part on the idea that AI readiness is not a single technology decision. Instead, it is an institutional capability that has to be developed across six pillars: strategy, data, security, governance, people, and infrastructure.
Audience responses offered a useful snapshot of how institutions are thinking about these pillars. When attendees were asked which pillar was strongest at their institution, Security, Infrastructure, and Data rose to the top.
When asked which pillar was weakest, the pattern shifted toward Strategy, Governance, and People Enablement.
That contrast suggests many institutions may feel more confident in the technical foundations of AI readiness than in the organizational, cultural, and decision-making structures needed to guide AI use effectively.
Data Governance Is Becoming Educational Infrastructure
The conference also highlighted the expanding role of data governance in academic medicine. Sessions addressed AI governance, dashboard development, assessment data, strategic planning metrics, research workflows, cybersecurity, and enterprise software implementation. Together, these topics pointed to a broader shift: data is no longer only a reporting asset, but is also becoming part of institutions’ core educational and operational infrastructure.
For medical schools and health professions programs, this shift has important implications. Many areas depend on timely, trustworthy, and well-structured data, including competency-based medical education, clinical assessment, learner advising, remediation, curriculum evaluation, accreditation, and strategic planning. Several conference conversations emphasized the need to work backward from the data and decisions institutions need, rather than beginning with the system implementation itself — a perspective especially relevant for assessment and curriculum platforms like Elentra, where the value of technology depends on how information is captured, structured, connected, and made usable across roles.
Educational Technology Must Fit Institutional Reality
Another recurring theme was the need to align technology innovation with institutional reality. Academic medicine institutions are under pressure to explore new tools, but they must do so within complex governance, compliance, budget, accessibility, security, and change management environments.
Sessions on educational technology adoption, SaaS governance, cloud migration, enterprise systems, and academic technology modernization underscored the need for clearer pathways between ideas and implementation. This is especially relevant as medical schools rely on multiple systems for learning management, curriculum delivery, assessment, scheduling, evaluation, advising, portfolios, and reporting. Without transparent intake processes, early stakeholder engagement, and shared governance, promising ideas can stall — or leave staff, faculty, and learners bridging disconnected systems manually.
Dashboards Are Only as Strong as the Systems Behind Them
Dashboards and analytics were another recurring focus. Several sessions explored how institutions are using dashboards to support strategic planning, learner assessment, advising, remediation, and operational visibility.
When designed well, dashboards can help institutions move from fragmented information to shared understanding. They can surface trends, identify gaps, support earlier intervention, and give leaders more confidence in their decisions. But dashboards are not a solution on their own. They depend on reliable data pipelines, consistent definitions, strong governance, thoughtful visualization, and trust among the people expected to use them. The goal is not simply to display more information. The goal is to make information usable in the moments when it matters.
Building Systems That Support the Future of Medical Education
The take-away sentiment from the conference was that the future of academic medicine technology will be shaped less by any single tool and more by the systems institutions build around those tools. AI, dashboards, cloud platforms, research technologies, assessment systems, and educational applications all have meaningful potential, but their impact depends on whether institutions can connect their systems to clear educational goals, responsible governance, sustainable workflows, and the people who need to use them every day.
For Elentra, these conversations closely align with our broader perspective on health professions education technology. Strong educational systems should help institutions bring structure to complexity, make data more usable, support longitudinal learner development, and give faculty and staff better visibility into the work already happening across their programs.
As academic medicine continues to navigate AI, data governance, and the growing complexity of educational technology, institutions need systems that help connect information, support better decisions, and bring greater clarity to learner and program outcomes. To learn more about how Elentra supports curriculum management, assessment, learner progression, and data-informed health professions education—contact us today.