From Consumers to Creators: AI and the Future of In-House Development in Higher Education
The Changing Role of Technology in Higher Education
Historically, higher education institutions have primarily engaged with technology as consumers. New platforms were typically introduced through vendor partnerships, purchased solutions, or externally developed systems intended to address specific operational or academic needs. For better or worse, the rapid evolution of artificial intelligence tools has begun to change this dynamic. AI is not only enabling new forms of automation and analysis. It is also lowering the barriers to creating applications in-house, allowing institutions to experiment with solutions tailored to their specific needs. In this context, digital transformation becomes a strategic imperative rather than a purely technical process. Digital transformation is understood as the strategic integration of digital technologies to reshape organizational processes, decision-making, and value creation (Sacavém et al., 2025). Additionally, emerging AI technologies can be understood as a mechanism with the potential of reshaping leadership roles by shifting leaders from information gatekeepers toward sensemakers who interpret increasingly complex data environments (Quaquebeke & Gerpott, 2023).
In my own work, this shift became evident while exploring how AI could support learning and decision making within a dental education environment. Rather than focusing solely on integrating external platforms that could somewhat fit our unique needs, we began experimenting with developing internal applications designed to address concrete institutional challenges. These early initiatives were not intended as fully scaled enterprise systems. Instead, they served as exploratory projects that allowed us to better understand how AI could extend our institutional capacity to design tools aligned with our educational goals. This post tells the story of two applications that are in an experimentation phase, and how they came to be.
Enabling a Learning Environment: The Virtual Patient Encounter Platform
One of the first experimental applications developed through this work was a virtual patient encounter practice center designed to support clinical communication training for D1 and D2 students.
The landing page of this application works as a case overview and case picker. A student decides who they might want to engage at any given time.
The platform allows learners to interact with AI-simulated patients representing diverse clinical profiles. Each case includes contextual details such as patient demographics, chief complaints, behavioral traits, and evolving clinical information. Students engage in conversational interactions, gathering relevant information in a manner similar to real patient interviews.
Interactions can take many forms. The student can read or play the voice of the actor as the case is introduced.
Beyond conversation practice, the system supports the development of documentation skills. After completing an interaction, learners can generate and refine SOAP notes, allowing them to practice translating clinical conversations into structured clinical records.
SOAP notes are free form, can be created with AI suggestions, saved, and exported for reporting conclusions to Faculty members that can advise on the encounter takeaways.
To be clear, this application was not created to replace traditional clinical training. Instead, it was designed to extend learning opportunities by providing a safe environment for repeated practice. Students can engage with multiple cases, make mistakes without real-world consequences, and reflect on their clinical reasoning processes.
Equally important, the project demonstrated how AI tools can enable small institutional teams to develop functional learning environments without relying entirely on external vendors. What once required extensive software development resources can now be prototyped through collaborative experimentation between educators, technologists, and AI-assisted design processes.
Aiding the development of a strategic Institutional Lens breakdown: Building a Program Intelligence Hub
A second initiative explored a different dimension of AI-supported development: improving organizational visibility for senior leadership.
In complex academic programs, critical information is often distributed across multiple systems. Admissions metrics, student progression data, curricular outcomes, and alumni performance indicators typically reside in separate platforms, making it difficult to develop a holistic understanding of program health. Leading effectively in such distributed environments requires a global mindset capable of integrating diverse perspectives and contextual information (Javidan et al., 2021).
To address this challenge, we began conceptualizing a centralized program intelligence hub designed to serve as a one-stop interface for institutional insights. The platform integrates data from multiple sources and presents key indicators related to admissions trends, student progression, competency attainment, program outcomes, and graduate trajectories.
A snapshot showing the program dashboard (all data presented comes from sample files).
While still evolving, this initiative reflects an important shift in how institutions can approach decision support systems. Rather than relying exclusively on static reports or fragmented dashboards, AI-assisted development makes it possible to design integrated environments that allow leaders to explore patterns dynamically and ask new types of questions about program performance.
All sections of this application provide meaningful data insights coming from different departments. Summarizing at a glance the happenings within the program.
As the development of the application continues, data governance, security, and data integrity are at the forefront of the conversations. What we are experiencing is the rapid advancement of AI-driven developments and the responsibility of providing sound solutions for data protection becomes a must.
More broadly, this work highlights how AI can support not only instructional innovation but also strategic decision making by enhancing institutional awareness and data accessibility. In this sense, AI can strengthen leadership reflexivity by expanding access to real-time information and enabling more evidence-informed decision processes (Matli, 2024). With applications such as the one presented above, preparing for accreditation visits would move from being a reactive exercise to a constant, proactive experience of program evaluation.
Lessons Learned: Developing Catalysts for Institutional Effectiveness and Curricular Capability
Reflecting on these experiences, one of the most significant insights is that the impact of AI in higher education may extend beyond efficiency gains or automation. Arguably, its most transformative potential lies in its ability to expand institutional capacity for creating solutions internally. As AI continues to lower technical barriers and enable educators and operational leaders to participate more directly in designing tools aligned with their contextual needs, we can only expect to see more creative developments coming from different higher education institutions.
These initiatives also reinforced that successful implementation depends less on technology itself and more on clarity of purpose. AI initiatives are most effective when embedded within supportive organizational cultures that encourage engagement, learning, and leadership alignment (Rozman et al., 2023). AI developmental efforts are most valuable when applied to clearly defined challenges rooted in authentic educational or organizational needs.
Finally, these projects revealed the importance of maintaining an experimental mindset. Early in-house applications are not necessarily polished enterprise products. Instead, they function as learning environments for institutions, helping teams develop new competencies in digital design, interdisciplinary collaboration, and data-informed innovation.
As AI continues to evolve, its role in higher education might increasingly shift from being viewed solely as an external technology to becoming an internal capability that supports both educational practice and organizational decision making. The future of AI in higher education may, therefore, depend less on the technologies institutions adopt and more on the internal capabilities they develop to design, govern, and sustain their own operational solutions.
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References
Javidan, M., Waldman, D. A., & Wang, D. (2021). How life experiences and cultural context matter: A multilevel framework of global leader effectiveness. Journal of Management Studies, 58(5), 1331–1362. https://doi.org/10.1111/joms.12662
Matli, W. (2024). Integration of artificial intelligence and leadership reflexivity to enhance decision-making. Applied Artificial Intelligence, 38(1), 1–22. https://doi.org/10.1080/08839514.2024.2411462
Quaquebeke, N. V., & Gerpott, F. H. (2023). The now, new, and next of digital leadership: How artificial intelligence will change leadership as we know it. Journal of Leadership & Organizational Studies, 30(3), 265–275. https://doi.org/10.1177/15480518231181731
Rozman, M., Tominc, P., & Milfelner, B. (2023). Maximizing employee engagement through artificial intelligent organizational culture. Cogent Business & Management, 10(2). https://doi.org/10.1080/23311975.2023.2248732
Sacavém, A., de Bem Machado, A., dos Santos, J. R., Palma-Moreira, A., Belchior-Rocha, H., & Au-Yong-Oliveira, M. (2025). Leading in the digital age: The role of leadership in organizational digital transformation. Administrative Sciences, 15(2), 43. https://doi.org/10.3390/admsci15020043