Artificial Intelligence (AI) in Learning and Discovery: AI Literacy & Competencies

What does it mean to be AI literate?

AI literacy is defined as a set of core competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, or in the workplace.¹

The Scale for the Assessment of Non-Experts

M.C. Laupichler et al. (2023) developed a three-factor model, identifying the “Scale for the assessment of non-experts' AI literacy” (SNAIL).² A non-expert is someone without a formal artificial intelligence or computer science education. SNAIL assesses competencies in three categories: technical understanding, practical application, and critical appraisal. These align with Karaca et al.'s (2021) AI readiness scale for medical students, which includes Cognition, Ability, Vision, and Ethic factors. Further research is needed to confirm if the findings apply to the broader non-expert population. Regardless, non-experts are not required to have the skill or capacity to explain all 31 competencies even if they are well-defined and perfectly crafted. Instead, it is recommended to choose three competencies in each category that show signs of room for growth and deeper understanding.

Technical Understanding (Factor 1)

I can describe how machine learning models are trained, validated and tested.

I can explain how deep learning relates to machine learning.

I can explain how rule-based systems differ from machine learning systems.

I can explain how artificial intelligence applications make decisions.

I can explain how reinforcement learning works on a basic level (in the context of machine learning).

I can explain the different between general (or strong) and narrow (or weak) artificial intelligence.

I can explain how sensors are used by computers to collect data that can be used for artificial intelligence purposes.

I can explain what the term 'artificial neural network' means.

I can explain how machine learning works at a general level.

I can explain the difference between supervised and unsupervised learning.

I can describe the concept of explainable artificial intelligence.

I can describe how some AI systems can act in their own environment and react to their environment.

I can describe the concept of big data.

I can evaluate whether media representations of AI (e.g., in movies or video games) go beyond the current capabilities of artificial intelligence technologies.

Practical Application (Factor 2)

I can explain why data privacy must be considered when developing and using artificial intelligence applications.

I can explain why data security must be considered when developing and using artificial intelligence applications.

I can identify ethical issues surrounding artificial intelligence.

I can describe risks that may arise when using artificial intelligence systems.

I can name weaknesses of artificial intelligence.

I can describe potential legal problems that may arise when using artificial intelligence.

I can critically reflect on the potential impact of artificial intelligence on individuals and society.

I can describe why humans play an important role in the development of artificial intelligence systems.

I can explain why data plays an important role in the development and applications of artificial intelligence.

I can describe what artificial intelligence is.

Critical Appraisal (Factor 3)

I can give examples from my daily life (personal or professional) where I might be in contact with artificial intelligence.

I can name examples of technical applications that are supported by artificial intelligence.

I can tell if the technologies I use are supported by artificial intelligence.

I can assess if a problem in my field can and should be solved with artificial intelligence methods.

I can name applications in which AI-assisted natural language processing/understanding is used.

I can explain why artificial intelligence has recently become increasingly important.

I can critically evaluate the implications of artificial intelligence applications in at least one subject area.

References

¹ Long D, Magerko B. What is AI Literacy? Competencies and Design Considerations. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM; 2020. p. 598–598.

² Laupichler MC, Aster A, Haverkamp N, Raupach T. Development of the “Scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis. Computers in human behavior reports. 2023;12:100338-