Embed AI literacy across disciplines by identifying intersections with existing content and using case studies, interdisciplinary modules, and collaborative projects¶

Strategy · All strategies
Evidence · no claims cited

Description¶

For curriculum design, the chapter recommends a transdisciplinary approach in which institutions identify "points of intersection between AI and existing disciplinary content and developing strategies to seamlessly integrate AI concepts." Concrete tactics include incorporating AI-related case studies in existing courses, developing interdisciplinary modules exploring AI applications in specific fields, and creating collaborative projects applying AI concepts to discipline-specific problems. New AI-focused programs should cover all seven knowledge dimensions and progress from foundational to expert levels.

Design Implications¶

Context¶

Requirements¶

• Institutional adoption of a transdisciplinary approach to curriculum design

Constraints¶

• The strategies are recommendations illustrated by examples, not tested interventions

Target Learners¶

• undergraduate and graduate students across academic disciplines

Target Learning Goals¶

• integrating AI concepts with disciplinary content and balancing technical skills with ethical and sociocultural awareness

Related Strategies¶

• Support faculty AI literacy through needs assessment, targeted training, teaching resources, and communities of practice
• Integrate computational thinking into existing disciplinary teaching across grade bands rather than adding it on
• Invest in dedicated instructional technology leadership roles and role-specific professional learning for AI literacy
• Integrate AI literacy education as an integral component of first-year curriculum development rather than an add-on
• Integrate AI into nursing curricula through AI-supported case discussions, structured clinical learning activities, and AI literacy training tailored to user groups
• Fund AI literacy initiatives integrating AI ethics into curricula and adapting assessments
• Share discipline-specific GenAI examples aligned with instructional needs to improve teacher adoption
• Embed GenAI literacy and ethical use directly in nursing curricula rather than prohibiting it
• Incorporate collaborative learning activities within AI literacy teaching, attending to activity modality and integration with other activities and assessments
• Embed AI literacy as a credit-bearing module and design adaptive systems with transparent, user-controllable recommendation interfaces
• Breadcrumb strategy: embed small AI-native skill exercises, nudges, and prompts across multiple courses throughout the four-year curriculum
• Embed critical AI literacy into curricula and institutional practices in higher education
• Incorporate AI literacy into educational-leadership preparation curricula
• Cultivate learners' critical AI literacy and integrity-focused group practices for GenAI-supported group work
• Embed AIL across disciplines by adding modules to existing Digital Literacy courses and offering interdisciplinary AI ethics and legal seminars
• Embed subject-specific web credibility modules into existing courses rather than creating standalone courses
• Integrate AI literacy into disciplinary core subjects rather than adding a new AI subject

Examples¶

-

Key Sources¶

• Kathleen Kennedy & Anuj Gupta. (2025). AI & Data Competencies: Scaffolding holistic AI literacy in Higher Education. Thresholds Volume 48, Issue 2. https://journals.ku.edu/thresholds