AI助力治理_运营与教学_2025年高等教育机构实践与政策调查（英）_62页_3mb

AI in Higher Education: Institutional Practices and Policies Survey Summary

Key Findings and Insights

This 2025 survey by the WICHE Cooperative for Educational Technologies (WCET) examines AI use in governance, operations, and instruction/learning. Key findings:

1. AI Adoption Status: Institutions are generally at a "slightly mature" AI use stage, with instruction/learning being the most prominent area. Use is expanding into operations and governance.
2. Primary Barriers: Lack of knowledge regarding AI among administrators, staff, and especially faculty is the main reason for non-adoption.
3. Shift in Focus: While "teaching critical digital skills" was the top benefit identified in 2023, "efficiency" is now seen as the most frequent benefit, signaling a move towards practical, operational uses alongside pedagogical applications.
4. Policy Development: A large majority of institutions have established, are developing, or plan AI policies, primarily focused on Academic Integrity/Plagiarism. A notable increase since the 2023 survey.
5. Diversity of Institutions: Responses reflect broad institutional type (4-year public > 2-year public) and size, though evidence suggests larger institutions may be slightly more advanced.
6. Introduction of New Concerns: Environmental impacts of AI are emerging as a new issue for the higher education sector.

Policies and Guidelines

• Development Trend: Seventy percent (70%) of institutions have existing AI policies or are actively working on their development or revision, showing a significant increase from the previous survey. Academic Integrity/Plagiarism remains the dominant policy focus.
• Policy Format: While policies exist, some institutions prefer flexible guidelines or frameworks due to AI's rapid evolution.

Challenges and Benefits

• Common Challenges: The primary barrier remains lack of AI knowledge/expertise among faculty, administrators, and staff (lack of expertise cited by 71%, distrust/skepticism by 66%).
• Evolving Concerns: Environmental impact (due to resources like water/energy consumption) is newly acknowledged. False accusations of plagiarism and generation of inaccurate information are significant concerns.
• Key Benefits: Efficiency and productivity ("Efficiency", "Workforce/career preparation for learners") are the highest reported benefits, followed by advantages related to data analysis and personalized learning.

Future Predictions and Recommendations

• Interviewee Predictions: AI adoption will increase, impacting instruction/learning, student advising/tutoring, admissions, and potentially credential requirements (e.g., reducing credits). Concerns about the digital divide and potential obsolescence of degrees remain.
• WCET Recommendations:
  1. Develop comprehensive AI policies/guidelines/fameworks (including students).
  2. Invest in faculty/staff/student AI literacy training.
  3. Establish AI task forces/committees.
  4. Provide incentives for AI adoption.
  5. Coordinate AI use across the curriculum.
  6. Address challenges proactively (expertise gaps, ethics, environment).
  7. Promote ethical and equitable use.
  8. Offer diverse student AI training.
  9. Utilize AI for operational efficiency and data analysis.
  10. Encourage exploration/experimentation.

Conclusion

The report highlights rapid AI adoption across higher education, driven by momentum in instruction/learning. While concerns about ethics, bias, academic integrity, faculty knowledge gaps, and costs persist, efficiency is increasingly cited as a key benefit. Proactive development of policies and guidelines is occurring. WCET emphasizes the need for ethical, equitable, and well-supported integration of AI into the core functions of higher education, moving from experimentation to operationalization, and advocates for addressing growing challenges like environmental impacts.