Defining AI Literacy for Higher Education
We begin with a general definition of AI literacy in an effort to establish a standing benchmark:
AI Literacy in Teaching and Learning (ALTL) involves understanding the fundamentals of how AI works; critically evaluating the application of AI tools in teaching, scholarship, and the management of educational priorities; and maintaining vigilance in evaluating tools and techniques to protect against bias, misuse, and misapplication of these powerful models. ALTL also demands a commitment to ethical usage, ensuring that AI tools are applied transparently and responsibly, with an awareness of their societal impacts.
As we break down this definition, we can see each area of concern represented:
- Technical: Technical understanding of how AI works.
- Evaluative: Critically evaluating AI tool applications and outputs, with an emphasis on developing robust AI impact assessment tools
- Practical: Effectively applying, integrating, and managing AI tools across teaching, scholarship, and educational administration.
- Ethical: Formulating and enforcing institutional strategies to safeguard against biases and the misuse or misapplication of AI technologies.
Each area can be broken down further as one prioritizes individual goals within one's own institution. Additionally, individual divisions or departmental units within an institution may prioritize certain areas in the framework over other areas. Figure 1 breaks down the AI literacy framework into each corresponding areas:
As educators and academic professionals, we must now consider how to foster both technical and practical AI literacy among students, faculty, and administration. This shift necessitates a reevaluation of curricula across disciplines—not just in computer science and related fields—to ensure that graduates are equipped with the AI literacy skills required in an increasingly AI-integrated world.