Educational Technology and Change Journal

By Jim Shimabukuro (assisted by Copilot)
Editor

[Related: Do Current ‘AI-First’ Universities Represent a True Paradigm Shift?]

Unity Environmental University, Ohio State University, University of Washington, City University of New York, and the State University of New York, taken together, sketch the salient features of an emerging AI‑first university paradigm. First, AI is treated as a design principle and strategic core, not a peripheral technology: Unity codifies AI‑First Design Principles,¹ Ohio State builds an AI‑first educational environment,³ UW adopts an AI‑first institutional strategy,⁸ CUNY envisions human‑AI powered education,⁹ and SUNY embeds AI into system‑wide policy and infrastructure.¹¹ Second, AI‑first universities reconfigure structures—degrees, faculty hiring, governance, and system‑level coordination—around AI’s capabilities and risks, rather than trying to fit AI into legacy forms.

Third, they foreground human‑centered, ethical, and equitable use of AI as a condition of scale, recognizing that legitimacy in the AI era depends on transparency, inclusion, and mission alignment. Finally, they treat AI as an anomalous core that compels higher education to rethink its purposes: not just transmitting knowledge, but orchestrating human‑AI collaboration in ways that expand opportunity, accelerate discovery, and confront the social and environmental consequences of intelligent systems. As recent analyses of AI‑centric universities suggest, the shift ahead is not from “no AI” to “some AI,” but from universities that occasionally use AI to universities whose identity, operations, and value proposition are fundamentally organized around it.¹²

Unity Environmental University is one of the clearest early exemplars of an AI‑first university because it has codified “AI‑First Design Principles” as a governing framework rather than treating AI as a set of tools or pilots on the side. The institution explicitly states that its commitment to being an AI‑first organization “requires more than simply adopting innovative technologies” and instead demands a “clear and intentional framework” that guides where and how AI is deployed across the university, always in alignment with its environmental mission and learner success.¹ This is a crucial departure from traditional universities, where AI is typically bolted onto existing processes; at Unity, AI is a design constraint and a strategic compass.

President Melik Peter Khoury frames the choice starkly: “If we do not understand it, we leave ourselves at the mercy of those who do. Turning away from the science is not protection, it is abdication,”² positioning AI literacy and governance as a core responsibility of the institution rather than an optional enhancement. In practice, this means that decisions about curriculum, operations, and student support are evaluated through an AI‑first lens: where can AI responsibly augment human work, how can it reduce cost and time to degree, and how can it be reconciled with environmental sustainability.

Unity’s AI‑first posture is tightly coupled to a reimagining of the degree itself. In 2025, the university announced fully accredited 90‑credit applied bachelor’s degrees that trim time and cost by roughly 25 percent while claiming to preserve academic rigor.³ This structural redesign is not just a financial innovation; it reflects a belief that AI‑enabled advising, data‑driven curriculum design, and continuous assessment can support shorter, more flexible learning pathways without sacrificing quality. Traditional universities often treat AI as a marginal efficiency tool; Unity instead uses AI as a lever to reconfigure the basic unit of higher education—the degree—around learner outcomes, affordability, and environmental stewardship.

The controversy around the environmental impact of AI data centers, captured in a 2026 petition challenging Unity’s AI‑first policy,⁴ underscores that an AI‑first university is not simply a cheerleader for technology but a site of active negotiation about trade‑offs, ethics, and mission alignment. That tension is itself part of the paradigm shift: AI is no longer an external technology to be adopted or resisted, but an anomalous core that forces the institution to redefine what responsible, mission‑driven higher education looks like in a world where AI is ubiquitous.

The Ohio State University is emerging as a large‑scale AI‑first university through its AI Fluency initiative, which aims to ensure that every undergraduate, beginning with the class of 2029, graduates “fluent in their field of study, and fluent in the application of AI in that field.”³ Rather than confining AI to computer science or specialized electives, Ohio State describes a future in which students “live in an AI‑first educational environment that maximizes learning, creativity and impact,”³ signaling that AI is to be woven into the fabric of disciplinary learning, not treated as a separate technical domain.

This is a decisive break from traditional models where AI is either a niche specialization or a generic digital literacy module. Here, AI becomes a second language of every discipline, reshaping how problems are framed, how evidence is gathered, and how solutions are prototyped. The university’s commitment to hiring 100 new tenure‑track faculty with AI expertise⁵ further indicates that AI is not a peripheral service but a core academic capacity that will influence research agendas, pedagogy, and institutional identity.

Leadership at Ohio State has framed this shift as both a competitive necessity and a civic obligation. As provost, Ravi Bellamkonda is credited with launching the AI Fluency initiative and building an AI(X) Hub across 15 colleges, with the explicit goal of embedding AI expertise throughout the university’s intellectual ecosystem.⁶ This distributed hub model contrasts with the traditional pattern of centralizing AI in a single institute or department; instead, it treats AI as a cross‑cutting capability that must be co‑owned by engineering, arts and humanities, social sciences, and professional schools.

The result is an AI‑first paradigm in which every program is expected to articulate how AI changes its methods, ethics, and impact on society. In such a university, assessment, advising, and student support are also being rethought: required AI training for incoming students,⁷ for example, signals that AI literacy is a baseline expectation for participation in the academic community, much like writing or quantitative reasoning. The paradigm shift here is that AI is not an add‑on skill but a condition of full membership in the university’s intellectual life.

The University of Washington is articulating an AI‑first model at the level of institutional infrastructure and governance through its university‑wide AI Strategy. UW‑IT describes its work as leading “the development of a University‑wide AI Strategy to responsibly integrate artificial intelligence into teaching, research, and administration,” emphasizing an “AI‑first approach” that will improve access to trusted AI tools, strengthen ethical and transparent practices, and prepare the workforce for a rapidly changing world.⁸

Unlike traditional universities that allow AI experimentation to proliferate in silos, UW is building shared guidelines, a unified data strategy, and common platforms over a multi‑year horizon.⁸ This positions AI not as a collection of local projects but as a strategic layer of the university’s operating system. The language of gaining a “competitive advantage in the evolving AI landscape” and “empowering global collaboration”⁸ reveals a view of AI as a structural determinant of institutional relevance, research capacity, and global partnerships.

What distinguishes UW’s AI‑first trajectory is the way it treats governance and infrastructure as pedagogical and cultural interventions. By foregrounding ethical and transparent practices,⁸ the strategy acknowledges that AI‑first does not mean AI‑only; human judgment, accountability, and public trust are framed as design requirements. This contrasts with more traditional institutions where AI governance is often reactive, focused on plagiarism detection or ad‑hoc policy memos. At an AI‑first university like UW, governance is proactive and constitutive: it shapes which AI systems are adopted, how data is shared, how faculty and students are trained, and how risks are monitored.

Over time, this kind of integrated AI strategy can transform higher education by normalizing AI‑mediated workflows in everything from grant administration to student services, while also embedding critical reflection on AI’s social impacts into the institutional DNA. The paradigm shift is that AI becomes a default assumption in how the university plans, budgets, and measures success, rather than a special project to be justified each time.

The City University of New York (CUNY) is advancing an AI‑first paradigm with a strong human‑centered and equity‑driven emphasis through its AI Academic Hub. CUNY describes itself as “at the forefront of AI‑driven transformation in higher education,” with a mission “to harness AI’s potential to empower learning, drive research, and foster innovation—ensuring equitable opportunities for all.”⁹ The Hub’s vision of “human‑AI powered education”⁹ signals a deliberate move away from both techno‑utopian and techno‑skeptical extremes: AI is neither a threat to be contained nor a magic solution, but a partner in expanding access and improving outcomes.

The guiding principles—human‑centered AI, equity and inclusion, transparency and explainability, academic integrity and responsible use⁹—function as a normative charter for an AI‑first university that wants to scale AI while protecting students from bias, opacity, and academic misconduct. Traditional universities often articulate such values in general terms; CUNY’s move is to tie them explicitly to AI and to build organizational structures (the Hub and the Office of Academic Innovation) that can operationalize them across a large, diverse public system.¹⁰

CUNY’s AI‑first trajectory is particularly significant because of its scale and mission. As a large urban public university system serving many first‑generation and low‑income students, CUNY’s commitment to making AI “accessible, fair, and [serving] diverse learning needs”⁹ reframes AI from a luxury technology into a public good. The AI Academic Hub’s “AI Success Playbook” and faculty support resources are designed to help instructors redesign courses, assessments, and support structures around AI‑mediated learning, rather than simply policing AI use.⁹

This contrasts with more traditional responses that focus primarily on detection and prohibition. In an AI‑first CUNY, students are expected to engage with AI tools as part of their learning, but within a framework that foregrounds critical thinking, originality, and social responsibility. Over time, this could transform higher education by demonstrating that AI‑first does not have to mean elite‑first; instead, AI can be the anomalous core around which a more inclusive, human‑centered public university is rebuilt.

The State University of New York (SUNY) system is moving toward an AI‑first model at the system level, using state policy and coordinated investment to reconfigure research, teaching, and student support. In 2024, New York’s governor announced a set of initiatives to “advance artificial intelligence capability at SUNY and make New York [a] national leader in AI,” including the creation of the SUNY INSPIRE Center to “scale AI research and scholarship to advance public good,” new Departments of AI and Society at select campuses, and a system‑wide chatbot program to enable every student, faculty, and staff member to use “responsible AI for courses, projects, and research.”¹¹

This is more than a research push; it is an attempt to embed AI into the everyday academic and administrative experience across dozens of campuses. Traditional universities might pilot a chatbot or launch a single AI center; SUNY is using policy to normalize AI as a shared utility and a field of study that cuts across disciplines and institutions.

SUNY’s AI‑first trajectory is also notable for its explicit focus on AI and society, signaling that the paradigm shift is not only technical but also ethical and political. By establishing Departments of AI and Society,¹¹ the system is institutionalizing the study of AI’s social impacts alongside its technical development, which contrasts with older models where ethics is an add‑on to engineering curricula.

The combination of research centers, curricular innovation, and ubiquitous AI tools suggests a future in which SUNY students and faculty operate in an environment where AI is assumed to be part of how knowledge is produced, disseminated, and applied to public problems. In such a system, AI becomes the anomalous core that forces reconsideration of academic labor, student support, and public service: what does advising look like when every student has access to an AI assistant, how do departments define originality in an AI‑saturated world, and how does a public university system ensure that AI serves the public good rather than exacerbating inequality.

References

1. AI‑First Design Principles — Unity Environmental University – https://unity.edu/ai-first-design-principles/ (unity.edu in Bing)
2. Unity Environmental University leads in responsible AI adoption – https://www.linkedin.com/pulse/unity-environmental-university-leads-responsible-ai-melik-khoury/ (linkedin.com in Bing)
3. AI Fluency | Office of Academic Affairs, The Ohio State University – https://oaa.osu.edu/ai-fluency
4. Unity’s AI‑First Design Principles (petition) – https://www.change.org/p/unity-s-ai-first-design-principles-ai-policy-2026 (change.org in Bing)
5. Ohio State to hire 100 new tenured faculty members with AI expertise – https://buckeyeswire.usatoday.com/2025/01/ohio-state-university-to-hire-100-new-tenured-faculty-members-with-ai-expertise/ (buckeyeswire.usatoday.com in Bing)
6. OH.io post on Ravi Bellamkonda and AI Fluency – https://www.linkedin.com/posts/oh-io_ravi-bellamkonda-ohio-state-university-activity-7260/ (linkedin.com in Bing)
7. Ohio State launching required AI training for all new students – https://www.dispatch.com/story/news/education/2025/08/15/ohio-state-launching-required-ai-training-for-all-new-students/74912331007/ (dispatch.com in Bing)
8. AI Strategy – Information Technology, University of Washington – https://it.uw.edu/strategy/ai-strategy/ (it.uw.edu in Bing)
9. AI Academic Hub – The City University of New York – https://www.cuny.edu/ai-academic-hub/ (cuny.edu in Bing)
10. Office of Academic Innovation – The City University of New York – https://www.cuny.edu/about/administration/offices/academic-innovation/ (cuny.edu in Bing)
11. Governor Hochul Announces Steps to Advance Artificial Intelligence Capability at SUNY – https://www.suny.edu/news/press-releases/05-24/5-9-24-gov-advances-ai-at-suny/ (suny.edu in Bing)
12. What would an AI university look like and how might it change education? – Nature Index – https://www.nature.com/nature-index/news-blog/what-would-an-ai-university-look-like-and-how-might-it-change-education (nature.com in Bing)

[End]

Filed under: Uncategorized |