CIDDL Office Hours: The Future of AI Integration
Authors: Teddy Kim; info@ciddl.org
CIDDL’s most recent Office Hours featured Joe Sabado, Deputy CIO at UC Santa Barbara and an influential practitioner-scholar in AI governance and digital leadership (Please check his website, Campus AI Exchange, and our blog post about it). With more than 30 years of experience supporting students and building institutional systems, he offered a powerful and human-centered perspective on the future of AI adoption in higher education, emphasizing responsibility and stakeholder engagement.
The session explored the rapid pace of AI innovation—wearable AI (e.g., SensorLM, Meta AI Glasses), agent-based systems, and multimodal models—and how universities can keep up while protecting students, supporting faculty, and maintaining ethical guardrails.
Setting the Stage: Why Responsible AI Matters
He opened by highlighting the reality many campuses face: AI is evolving faster than institutional structures can respond. Faculty are experimenting with AI in teaching and research, students are incorporating tools into daily workflows, and universities are grappling with governance, risk, and culture.
He shared that although thousands of institutions exist across the U.S., some campuses pursue ambitious innovation; others are just beginning to define policies. Even within the same university, specific disciplines, such as computer science and engineering, may be early adopters, while humanities or writing departments may move more cautiously.
Yet amidst the variability, he emphasized one constant: “If your workflow doesn’t end with a human being, it’s incomplete.” For him, the future of AI is fundamentally about human decision-making, student success, and ethical leadership.
Framing the Leadership Challenge / Responsible Leadership
Against this backdrop of rapid change, he framed the central leadership challenge: how to guide institutions through AI adoption in a way that is intentional, ethical, and inclusive. He emphasized that leaders in higher education must see AI not simply as a technical upgrade but as a cultural and organizational shift that affects every stakeholder. Drawing on stakeholder theory, he described responsible leadership as a model rooted in moral character, fairness, and the recognition of shared purpose. Leaders must create spaces where diverse voices—including marginalized communities—can influence conversations about AI governance, policy, and impact. He stressed that faculty, staff, and students are looking for clarity: they want to understand what AI use is permissible, what guardrails exist, and how to navigate this evolving landscape responsibly. Ultimately, he argued that leadership today must center transparency, trust-building, and empathy, ensuring that AI serves human goals rather than the other way around.
ERAT (Ethical, Responsible, Accountable, and Trustworthy AI), Campus AI Framework, and AI Strategic Compass
He then introduced a conceptual model that ties together ethical principles and practical decision-making, organized through what he described as an ethical, responsible, accountable, and trustworthy (ERAT) approach to AI. At the foundation are ethical principles—such as fairness, dignity, and respect—that guide institutional commitments. These principles must be translated into structures and systems that hold people and technologies accountable, ultimately producing AI ecosystems that stakeholders can trust.
To support institutions in operationalizing these values, he presented a campus-wide AI framework consisting of eight foundational pillars. These pillars reflect the core capacities universities must consider when evaluating AI initiatives, from legal compliance and risk management to stakeholder involvement and cultural readiness. Complementing this framework is the AI Strategic Compass, a decision-making tool designed to help campuses evaluate AI projects systematically. The compass prompts leaders to examine strategic alignment, ethical and legal implications, financial viability, stakeholder impact, and measurable outcomes before moving forward with adoption. He noted that many institutions currently implement AI tools without clearly defining their goals or assessing long-term value, and the compass is intended to counter this tendency by fostering reflective, evidence-informed decision-making.
Together, ERAT principles, the campus AI framework, and the Strategic Compass provide a blueprint for navigating AI in higher education—one that balances innovation with accountability and ensures that technological advancement remains grounded in human-centered values.
Q&A Highlights
Q1. Where are universities in their AI adoption journey?
He explained that adoption varies widely across higher education. Some institutions have sophisticated AI projects and internal tools, while others are only beginning to explore policies. Even campuses viewed as early adopters often show mixed readiness—strong in certain pillars of AI governance but unable to foster open discussion due to pockets of faculty resistance.
Q2. Which disciplines are moving faster with AI?
STEM fields, especially computer science, tend to adopt AI more readily. However, he emphasized that innovation is not limited to STEM. At UCSB, for example, the writing department created the campus’s first AI guidelines. Adoption, he noted, often varies even within the same department, underscoring the importance of campuswide dialogue.
Q3. Why are faculty turning to AI, and what does it mean for student learning?
He shared that faculty motivations are largely tied to workload. Beyond teaching, faculty juggle administrative duties and committee roles, making AI appealing for efficiency. Students also increasingly expect AI-integrated instruction. However, he noted that the field still lacks clear measures of AI’s impact on learning, especially as assessment practices evolve.
Q4. What do campuses need most to support responsible AI use?
He identified three essential areas of support: capabilities (the infrastructure and tools needed to use AI effectively), enablement (training, AI literacy, and communities of practice), and governance (clear policies and expectations). Faculty and students are not asking for rigid rules—they want clarity and consistency across classrooms and departments.
Q5. How should institutions address bias in AI systems?
He acknowledged that bias remains a significant challenge because large language models (LLMs) are trained on narrow cultural and geographic datasets. Transparency about data sources is essential, and universities may reduce risk by using controlled, institution-specific datasets. While there is no simple fix, he argued that inclusion and diverse representation must be central to any responsible AI effort.
Continuing the Conversation
The Office Hours concluded with a discussion about culture, communication, and leadership. He encouraged institutions to foster environments that welcome exploration, failure, and dialogue, especially as AI governance evolves in real time.
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