Accrediting bodies and professional organizations should provide AI policy frameworks integrating academic integrity, data ethics, and equitable access, and programs should invest in faculty development¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description¶

The article recommends that, given substantial variation in policy structure and content, "accrediting bodies and professional organizations can help reduce variability by providing AI policy frameworks that integrate best practices in academic integrity, data ethics, and equitable access." It further recommends that programs "monitor regional policy trends and invest in faculty development to support AI-enabled graduates," and that standardizing and contextualizing institutional AI policies would help programs operationalize frameworks addressing HI/HIM-specific requirements.

Design Implications¶

Context¶

Requirements¶

• Institutional AI policies currently exist in advisory (guideline) form across most programs, providing a base to standardize.

Constraints¶

• Findings are limited to CAHIIM-accredited HI/HIM master's programs in the United States and may not extend to other disciplines or international contexts.

Target Learners¶

• graduate HI/HIM students
• faculty in accredited HI/HIM programs

Target Learning Goals¶

• responsible AI use
• AI literacy
• academic integrity in AI-supported coursework

Affordances¶

• Three Domains Academic Ai Policy Curriculum Governance

Claims¶

• Equitable-access and digital-inclusion themes are minimally represented in HI/HIM AI policies [+W]
• Academic integrity terms dominate HI/HIM AI policy content while healthcare regulatory references (HIPAA, FERPA, IRB) appear infrequently [+W]

Related Strategies¶

• Build ethical AI integration on four coordinated levels: faculty professional development, ethics curricula, clear institutional guidelines, and interdisciplinary updating
• Pursue inclusive digital strategies, educator professional development, and ethical AI governance for AI in adult education
• Institutions should respond to GenAI with clear governance frameworks and structured professional training programs for staff.
• Strengthen institutional policies, ethics education, verification, disclosure, and data protection to support ethical GenAI use
• Adopt AI-PBL frameworks with institutional and policy-level support and teacher training investment

Examples¶

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Key Sources¶

• Eldredge C, Dolezel D, Jones J, Vooppala N, Bronsburg SE. (2026). Institutional approaches to artificial intelligence policy and guidance in health informatics and information management education: emerging trends and inconsistencies. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocag150