AI Playbook

The AI Playbook that emerged from the DataSci+AI Forum was intended to serve as a living, shared guide for how the University of Pittsburgh approaches AI across research, teaching, and operations. Rather than a static policy document, it was designed as a signal-to-action framework—capturing real-time insights from faculty, students, and partners and translating them into actionable principles.

Built through the Forum’s “Signals → Patterns → Principles” process, the Playbook synthesizes how AI is currently being used, where gaps and opportunities exist (especially in strategy and teaching), and what responsible, effective use should look like across contexts. Its goal is to align distributed expertise across the university, support better decision-making, and help Pitt move from experimentation to coordinated, trustworthy implementation of AI.

Ultimately, the AI Playbook is meant to function as both a reflection of the ecosystem and a tool for shaping it—guiding digital leadership, informing institutional strategy, and ensuring that AI innovation at Pitt is grounded in responsibility, transparency, and real-world impact.

How Does the Availability of GenAI Tools Shape How We Design Instructional Experiences and Assess Competencies?

This group explored how the increasing availability of generative AI tools is transforming instructional design and assessment. The discussion highlighted shifts toward skills-based learning, critical thinking, transparency in AI use, and rethinking traditional assessment models.

Participants highlighted that the availability of generative AI tools is reshaping both instructional design and assessment. Key themes included the need to redesign assessments, emphasize critical thinking, and establish clear norms for AI use, and also trust within the system. The group underscored the importance of using AI to support learning while addressing challenges related to equity, transparency, and evaluation.

How Can Pitt Ensure AI Innovation Benefits the Broader Pittsburgh Region and Workforce?

This group explored how Pitt can ensure that AI innovation meaningfully benefits the broader Pittsburgh region and workforce. The discussion emphasized community engagement, workforce development, access to training, and cross-sector collaboration as key drivers of regional impact.

Participants emphasized that ensuring AI innovation benefits the Pittsburgh region requires expanding access to AI education, strengthening workforce development, and building meaningful community partnerships. The group highlighted the importance of applying AI to real-world challenges, engaging communities in decision-making, and fostering collaboration across institutions to create lasting regional impact.

What Are Examples of Responsible AI Use Cases in Research, Teaching, and Operations at Pitt?

This group explored concrete examples of how AI is currently being used—or could be used—responsibly across research, teaching, and operations at Pitt. The discussion highlighted a strong interest in practical, real-world applications, alongside the need to ensure these uses are ethical, transparent, and human-centered.

Participants highlighted the importance of identifying and sharing concrete examples of responsible AI use across research, teaching, and operations. Key themes included using AI to augment human expertise, ensuring strong human oversight, and developing clear, context-specific guidance. The group emphasized that responsible AI is best demonstrated through real-world applications that are transparent, ethical, and adaptable.

What Governance and Transparency Practices Are Needed for Responsible AI Adoption at the Institutional Level?

This group explored the governance and transparency structures needed to support responsible AI adoption at Pitt. The discussion highlighted the importance of clear guidelines, defined accountability, inclusive decision-making, and consistent communication to ensure AI is used responsibly across the institution.

Participants emphasized the need for clear, transparent, and inclusive governance structures to support responsible AI adoption at Pitt. Key themes included defining accountability, developing robust guidelines, and improving communication around AI use. The group highlighted that effective governance must be adaptive, collaborative, and grounded in real-world applications.

What Does “Digital Leadership” Mean for Our University and Regional Leaders?

This group explored how “digital leadership” is defined in the context of both the university and the broader region. The discussion emphasized access, communication, innovation, community impact, and the responsible use of data and AI as core components of effective digital leadership.

Participants defined digital leadership as a balance of technical capability and human-centered communication. Key themes included the importance of data-informed decision-making, accessible infrastructure, and strong communication skills. The group emphasized that digital leaders must connect innovation to real-world impact, both within the university and across the broader Pittsburgh region.