Task Force Membership
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GSSC/Distance Learning/IT Laura Wight (Chair) Tara Mishra (Distance Learning) Matthew Gormley (Systems Librarian) Renata Torrez (IT) Deans Lee Hurren (CET) David Steffens (CFA) CFA Greg Senn (Art) Parker Yi (CFA Student Rep) CET Olga Gould Kalynn Baldock Kathleen Wagner Tracie Campbell Jihyun Woo |
CLAS Sarah Lonelodge (Language & Literature) Jovana Gomez (Language & Literature) Erik Stanley (Anthropology) Kris Kuhlmann (Nursing) Eduardo Ceh Varela (Computer Science) Omar Camarillo (Social Sciences) Stephen Washington (Student Rep) COB Tara Vander Dussen Tim Winrow Zeek Niu Thallan Schwartz (COB Student Rep) |
The task force began its work in late September 2023 and will conclude in December 2024 with the potential for further recommendations at that point in time.
In recent years, the technology underpinning generative AI, e.g., ChatGPT has evolved rapidly with the potential to radically transform numerous industries, including education. With the ability to generate human-like responses based on queries, generative technologies such as large language models (LLMs) pose both opportunities and challenges in higher education. ENMU recognizes the importance of proactively navigating this complex landscape and recommending necessary guidelines for the use of generative AI by establishing a task force for studying its potential applications and effects. The insights and guidelines developed by this task force will better enable us to deal with opportunities and challenges posed by AI technologies.
By fostering an AI-ready learning environment for our university community, we aim to undertake a leadership role in preparing our students for an AI-enabled future. This task force will create opportunities for dialogue and explore the ramifications of generative AI on faculty scholarship, pedagogy, learning outcomes, staff workflow, and university administration with the goal of developing strategies for effectively integrating these technologies into our university’s ecosystem. The ultimate goal is to equip our students with the skills and knowledge required to thrive in an AI-enabled world after acquiring higher education at ENMU.
The primary objectives of the task force are as follows:
a) Investigate Consequences: To conduct a comprehensive analysis of the potential benefits and challenges associated with implementing generative AI technologies in higher education, including impacts on student learning, academic integrity, faculty roles, staff efficiency and institutional resources. Fall 2023
b) Explore Possible Applications and Create Opportunities for Dialogue: To identify and assess the diverse applications of generative AI within the context of higher education, and to develop and host a series of open forums, panel discussions or other opportunities for facilitating dialogue on generative AI. Fall 2023/Spring 2024
c) Develop Guidelines and Policies: To study potential strategies for the integration of generative AI into the university's systems, and to formulate clear and ethical guidelines for the responsible use of generative AI tools and systems on our campus. Spring/Summer 2024
d) Facilitate Faculty and Staff Training: To recommend training programs and resources for faculty and staff to understand and effectively integrate generative AI technologies into their workflow, teaching methodologies, and research practices as appropriate. Summer/Fall 2024
e) Enhance Student Training: To propose initiatives that will empower students to develop essential AI-related skills, including critical thinking, data literacy, and an understanding of ethical AI usage, and to explore potential collaborations with industry partners for providing students with real-world AI experiences. Summer/Fall 2024
Focus Areas for Task Analysis and Training Needs Assessment
1. Adapting Assessment & Learning Outcomes in a Generative AI World
Evaluate how faculty can revise academic assessments and learning outcomes in light of generative AI. Investigate the implications of AI on academic integrity and the emergence of new skills necessary in an AI-driven workforce. Analyze how generative AI can support faculty in designing innovative curricula and educational experiences.
2. Pedagogy
Explore shifts in teaching and learning methodologies owing to the integration of AI technologies. Investigate new pedagogical approaches that leverage AI capabilities for improved student learning, enhanced accessibility, and inclusivity for students with diverse learning needs.
3. Employee Efficiency Opportunities and Resource Analysis
Examine the potential of generative AI to streamline administrative tasks, reduce workload, and improve overall efficiency among university employees. Evaluate the technological infrastructure and resources required to support the implementation of generative AI solutions on campus.
4. Ethical Considerations and Faculty Use Cases
Examine the ethical implications of generative AI usage in higher education and establish protocols to ensure fairness, transparency, and respect for privacy. Evaluate the benefits and potential risks of generative AI in various faculty activities, such as research, syllabus construction, homework question creation, and discussion board prompts.
5. General Benefits to Research
Investigate the broad benefits of generative AI in research activities within the university, and how these may influence research quality, efficiency, and outcomes.
The task force will deliver the following outputs:
a) A comprehensive report outlining the findings from the investigation into the consequences and potential applications of generative AI at ENMU. Spring 2024
b) A ‘Guidelines and Policies’ document that provides a clear framework for responsible and ethical use of Generative AI on the ENMU campus. Summer 2024
c) A Faculty and Staff Training Program comprising workshops, seminars, and resources to empower faculty and staff in effectively using generative AI in their teaching, research and workflow. Fall 2024
d) A list of Student Training Initiatives, including curricular enhancements and extracurricular opportunities to equip students with AI-related skills. Fall 2024
e) A set of recommendations for technological upgrades and investments necessary to support generative AI implementation. Fall 2024