Purpose
Artificial intelligence advances have created a paradigm shift in university education where strategic human-AI collaboration enables unprecedented capabilities in knowledge work through complementary cognitive strengths. Research demonstrates that humans and AI possess asymmetric information processing and capability profiles that, when properly integrated, produce superior outcomes in knowledge-intensive tasks. We are transforming university education to prepare students for this emerging collaborative knowledge era by developing both technical AI literacy and the critical thinking skills necessary to lead human-AI teams across disciplines. Our approach emphasizes building students' capacity to leverage AI's computational strengths while maintaining human agency in areas requiring ethical reasoning, contextual judgment, and creative problem-solving — skills essential for leadership in the University of Pittsburgh's academic disciplines.
With that vision in mind, developing GenAI competency is an essential first step to maintaining the University of Pittsburgh's competitiveness as a leading research institution and ensuring the long-term success of our students in the workforce. The job market is being rapidly reshaped by GenAI and other emerging technologies, with new roles, workflows, and expectations for technological fluency across nearly every discipline and sector. Equipping students, faculty, and staff with the capacity to understand, apply, and critically evaluate these tools will not only position our graduates for success in this evolving landscape but will also enable the university to lead in innovation, research impact, and developing the next generation of thinkers and changemakers. We must strategically grow capacity to engage and lead human-AI collaboration.
Individuals from different role groups at the University of Pittsburgh — including students, staff, faculty, and administrators — are using current and emergent GenAI to facilitate teaching, enhance learning, conduct research, and streamline administrative tasks. However, the disciplined use of GenAI and other emerging technologies is not yet widespread, despite their outstanding potential to accelerate learning, advance scientific discovery, and improve administrative workflows across the university.
While there are numerous efforts underway to innovate, critically evaluate, and apply GenAI to core Pitt functions, there is currently no unifying way to both prioritize efforts and facilitate collaboration across the university. The LSET Hub has synthesized leading AI literacy and competency models into an evidence-based, unifying framework that describes GenAI competencies in the present moment and for various core functions at Pitt. This framework draws on widely adopted models created by leading organizations at the forefront of technological innovation and engaged in addressing the ethical implications and responsible use of emerging technologies, and specifically, GenAI.
The purpose of this framework is to serve as a shared point of reference that can be adapted and activated across the university over time. It is intended for use by different units and role groups to guide thoughtful, context-specific discussions about how to assess current capabilities, identify gaps, consider ethical and responsible use practices, set priorities for advancing readiness, and benchmark progress. By offering a structured, common language for understanding the knowledge, skills, attitudes, and ethical considerations required for effective and responsible use, this framework can help departments and role groups co-develop strategies that align with their unique missions while contributing to a coherent, institution-wide approach and resource sharing. We ultimately envision this framework helping us, as an institution, focus effort, determine responsibility areas, co-design resources, and share resources for rapid scaling of effective approaches.
What is the LSET Hub?
The Learning Sciences & Emergent Technologies (LSET) Hub is a cross-university collaboration — anchored by the Learning Research & Development Center (LRDC), the School of Computing & Information (SCI), the University Center for Teaching & Learning (UCTL), the Dietrich School of Arts & Sciences (DSAS), and the Health Sciences Information Technology — that organizes people and resources to develop, test, refine, and scale learning-sciences-infused uses of emergent technologies (beginning with generative AI and extending to immersive media) in teaching and scholarship. Grounded in a research-practice partnership model and a commitment to equity, ethics, and disciplinary nuance, the Hub curates evidence-based guidance and tools, connects faculty and students across nodes of innovation, and convenes a community of practice that shares strategies, prototypes, and results. Our aim is to help Pitt lead nationally in the ethical, evidence-based integration of digital technologies, advancing student success and faculty excellence while aligning with the Digital Future goals in the Plan for Pitt 2028. The LSET Hub includes the following core contributors:
- Jennifer Iriti- Co-Chair, LRDC and Pitt Research
- Elizabeth Votruba-Drzal- Co-Chair, LRDC & Psychology
- Bruce Childers- Co-Chair, SCI
- John Radzilowicz- Co-Chair, UCTL
- Raquel Coelho, LRDC & SCI
- Alan Lesgold, LRDC & UCTL
- Lindsay Clare Matsumura, LRDC and School of Education
- Yiwen Lin, LRDC
- Nathan Ong, LRDC & UCTL
- Robert Ackerman, UCTL
- Angela Allie, IFL
- Charles Perfetti, LRDC & Psychology
- Mike Bridges, UCTL (Co-chair Emeritus)
- Liz Rangel, LRDC
- Gayle Rogers, DSAS- English Department
- Walt Schneider, LRDC
- Ashley Stewart, Health Science Information Technology
- Alison Slinskey Legg, BE-STEM
- Michael Arenth, UCTL
- Bridget Ridge, UCTL
- Tony Petrosky, IFL
How do we hope you'll engage with this GenAI Competency Framework?
- Critically reflect on the proposed framework from your organizational context and role to offer feedback to strengthen the framework content and/or communicate how it might be adapted to better fit with your needs. We hope that through socializing this draft, we can build a consensus framework that works for Pitt overall.
- Help us understand where and how your efforts align with the framework. Is there a particular segment of the framework where your GenAI efforts are located? Is there a particular competency that you see as mission-critical for your unit/department/role right now? The goal of this framework is not for LSET or any one group or organization to take ownership of GenAI readiness overall, but to communicate where existing work is already underway so that we can acknowledge those contributions, make the learning and resources visible, and scale what's working.
- Let us know what practices, resources, or development efforts already exist and how they connect with the framework. Our hope is to use this framework to map effort and resources across the University to help us spread effective practices and connect folks more quickly.
- Where is there an opportunity for disciplined study of innovations? We'd like to leverage Pitt's innovation and implementation efforts to learn how to do GenAI readiness work in ethical and learning sciences-aligned ways.
- Are you interested in working in an interdisciplinary group to develop approaches, practices, or resources for specific competencies? The LSET Hub plans to convene co-design workgroups for specific competencies--for example, form a group to co-design how to leverage GenAI in the assessment of learning.
Goal Statement for the GenAI Competency Framework
The framework will be used by all university community members to build generative artificial intelligence competency while examining its use in societal, political, cultural, ideological, and intrapersonal contexts. This framework is a living, participatory tool to promote collaboration with others within the FATE framework (fairness, accountability, transparency, and explainability [ethical factors]). Specifically, our goals are:
- To provide a framework for supporting the human-centric integration, adoption, and responsible use of GenAI into the university community across roles and functions.
- To unify and coordinate efforts underway across individuals, units, affinity groups, Schools, and Centers.
- To provide a framework to organize and distribute resources that facilitate more widespread collaboration in building GenAI competency.
DRAFT GenAI Competency Framework*
| General GenAI Competencies 2025-2026 | |
| Understand | ✓ Know what GenAI is, how it works in general terms, and its limitations. ✓ Recognize risks (bias, misinformation, inequality) and broader societal impacts. ✓ Be aware of how GenAI is used in your field and in the wider world. ✓ Appreciate principles for ethical use and implications of GenAI. |
| Use & apply | ✓ Use institution-approved AI tools to support communication, productivity, and problem-solving. ✓ Apply AI responsibly: protect privacy, avoid sensitive data, follow policies, install accountability mechanisms. ✓ Respect ethical values (fairness, inclusivity, sustainability) when using AI. ✓ Know when to acknowledge or disclose AI use. ✓ Collaborate with AI tools and with human + AI teams. |
| Evaluate, design & create | ✓ Critically assess AI outputs for accuracy, bias, and reliability. ✓ Reflect on ethical implications of AI use in your work. ✓ Contribute ideas for responsible and inclusive AI practices or projects. ✓ Stay adaptable and informed as tools and policies evolve. ✓ Monitor and adapt practices to ensure responsible use at scale. |
*This framework is a synthesis of AI frameworks and research. It relies on the OECD and European Union AI Literacy Framework for primary and secondary education (2025), the Digital Promise AI Literacy Framework (2024), and the UNESCO AI competency framework for students (2024).
Generative AI Competencies for Specific Work Functions
University personnel and student engage in a range of functions from teaching to learning to administrative and research. For each of these functions, we have identified the current most critical five competencies to be nurtured to leverage GenAI.
Teaching Functions
- Course & lesson design with GenAI and infusing GenAI
- Instructional materials & assessment creation
- Formative feedback & scaffolding frameworks
- Personalized & inclusive learning support with GenAI
- Classroom engagement & active learning with GenAI
Learning Functions
- Idea generation & exploration
- Tutoring & concept clarification
- Study, practice & prepare
- Scaffolding communication
- Problem solving & strategy support
Administrative Functions
- Information management & communications
- Document & report generation
- Application & records processing
- Data analysis & decision support
- Scheduling & resource coordination
Research Functions
- Literature review & synthesis
- Research design
- Writing & communication support
- Data analysis & collection
- Facilitating collaboration
References
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100101.
Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO. https://doi.org/10.54675/JKJB9835
Mills, K., Ruiz, P., Lee, K., Coenraad, M., Fusco, J., Roschelle, J.& Weisgrau, J.(2024, May). AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology. https://doi.org/10.51388/20.500.12265/218
OECD (2025). Empowering learners for the age of AI: An AI literacy framework for primary and secondary education (Review draft). OECD. Paris. https://ailiteracyframework.org
Responsible Data Science (July 3, 2025). How Responsible Data Science Delivers Digital Leadership in the Age of AI. Available: https://www.linkedin.com/pulse/how-responsible-data-science-delivers-digital-0tuye/
