• AI Literacy

  • AI Literacy

AI literacy for a typical individual is the ability to comprehend, interact with, and make informed decisions regarding artificial intelligence technologies in daily life. It involves understanding the basic principles of AI, recognizing its applications, and being aware of ethical, social, and privacy implications while responsibly engaging with AI systems.

AI literacy refers to the understanding and utilization of artificial intelligence (AI) technologies. It encompasses the ability to critically evaluate AI, understand its capabilities and limitations, effectively communicate and collaborate with AI systems, use AI as a tool in various contexts, and understanding privacy and safety-related issues (Chan & Colloton, 2024; Kong et al., 2021b; Long & Magerko, 2020). Fostering AI literacy is essential in academic, workplace, and daily life contexts. At the same time, it is necessary to also recognise and understand the challenges that exist due to rapid AI development, resource availability, and accessibility constraints.

An AI literate learner, according to Chan & Colloton (2024), should comprehend basic AI terminology, utilize AI applications, differentiate realistic expectations from exaggerated hype, understand AI safety and security, and use AI responsibly. Moreover, it should be noted that one’s context or role may influence an individual’s understanding of AI literacy.

AI literacy is important as helps individuals identify misconceptions about AI, use AI ethically and responsibly, enhance career opportunities, and develop innovative and creative products. Misconceptions can arise from swift technological advancements and exaggerated marketing strategies. Understanding AI’s concepts, applications, and capabilities is crucial for responsible interactions, while ethical awareness safeguards privacy and ensures unbiased decision-making. AI literacy can further enhance competitiveness in the job market, as well as foster innovation by leveraging AI’s creative potential in problem-solving (Eapen et al., 2023) and generating media.

AI literacy can be developed and supported through various approaches, including though a combination of lectures on AI concepts and principles, followed by hands-on applications of the taught material for students to apply their knowledge and develop both AI literacy and interest (Perchik et al., 2023; Shih et al., 2021). Laupichler et al. (2022) also found that AI literacy can be cultivated through individual courses or broader curricula, with governments recognizing the need for strategies to enhance AI literacy among citizens, while Chan (2023) identifies ten key areas for planning AI policies in higher education institutions, including addressing academic misconduct, ensuring data privacy, evaluating AI implementation, and providing training for AI literacy.

Assessing AI literacy can be complex and diverse, influenced by factors including learning contexts and institutional policies. Kong et al. (2021a) used tests and surveys to evaluate students’ learning outcomes in an AI literacy course, looking at their understanding of AI concepts, their self-perceived level of AI literacy, and the extent to which students felt empowered by their AI use. These measures assessed students’ progress in understanding AI concepts and their perceptions of AI’s impact on their lives. Meanwhile, Kong et al. (2022) employed reflective writing as an evaluation measure in an AI literacy program, where students reflected on their understanding of AI and ethical considerations.

In conclusion, developing AI literacy requires a comprehensive approach that encompasses various considerations and strategies. By implementing effective teaching methods, assessing knowledge and perceptions, and promoting critical thinking, individuals can enhance their AI literacy and navigate the AI-driven world effectively and responsibly.

Understanding the Five Aspects of the AI Literacy Framework
AI Concepts AI Application AI Hype vs. Reality AI Safety and Security Responsible AI Usage
Familiarity with basic terminology

  • Artificial narrow / general / super intelligence
  • machine learning
  • machine intelligence
Awareness of common AI applications, APIs and plugins

  • virtual assistants
  • recommendation systems
  • facial recognition
Differentiate between the potential of AI and the marketing hype

realistic expectation of what AI can / cannot do

Awareness of potential security risks

  • possible threats to personal data
  • misuse of technology
Responsible use of AI applictions and understand and take steps to address limitations of AI systems

  • fact-checking information
  • consider ethical implication
  • question the reliability of AI-generated content

(Chan & Colloton, 2024)

Building AI Literacy: Five Essential Aspects for Students to Navigate the World of Artificial Intelligence

In order to navigate the world of artificial intelligence effectively, it is crucial for students to develop AI literacy. Here are five key aspects that students should be aware of (Chan & Colloton, 2024):

  • AI Concepts

    Students should familiarize themselves with basic AI terminology and principles to gain a better understanding of how AI systems function.
  • AI Applications

    Awareness of common AI applications and their presence in everyday life is essential. This includes basic understanding technologies like virtual assistants and facial recognition.
  • AI Hype versus Reality

    Students should be able to differentiate between the potential of AI and the marketing hype surrounding it. This will help them develop realistic expectations of what AI can and cannot accomplish.
  • AI Safety and Security

    Understanding the potential security risks associated with AI applications is crucial. Students should be aware of threats to personal data and the potential for misuse of AI technology.
  • Responsible AI Usage

    Developing a sense of responsibility when using AI applications is vital. This involves considering the limitations of AI, fact-checking information, addressing ethical implications, and questioning the reliability of AI-generated content.

For more details, please check the course “AI Literacy for Education”: https://tl.hku.hk/2023/08/ai-literacy-for-education/

  • Chan, C.K.Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20. https://doi.org/10.1186/s41239-023-00408-3
  • Chan, C. K. Y. & Colloton, T. (2024). Generative AI in Higher Education: A ChatGPT Effect. Routledge.
  • Eapen, T.T., Finkenstadt, D.J., Folk, J., & Venkataswamy, L. (2023, July – August). How Generative AI Can Augment Human Creativity. Havard Business Review. Retrieved 5 September, 2023, from https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity
  • Kong, S., Cheung, W.M., & Zhang, G. (2021a). Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building. Computers in Human Behavior Reports, 7, 100223. https://doi.org/10.1016/j.chbr.2022.100223
  • Kong, S., Cheung, W.M., & Zhang, G. (2021b). Evaluation of an artificial intelligence literacy course for university students with diverse backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026
  • Kong, S., Zhang, G., & Cheung, M. (2022). Pedagogical Delivery and Feedback for an Artificial Intelligence Literacy Programme for University Students with Diverse Academic Backgrounds: Flipped Classroom Learning Approach with Project-based Learning. Bulletin of the Technical Committee on Learning Technology, 22(1), 8-14.
  • Laupichler, M.C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
  • Long, D., & Magerko, B. (2020, April). What is AI Literacy? Competencies and Design Considerations. In CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp.1-16). Association for Computing Machinery, New York, US. https://doi.org/10.1145/3313831.3376727
  • Perchik, J.D. , Smith, A.D., Elkassem, A.A., Park, J.M., Rothenberg, S.A., Tanwar, M., Yi, P.H., Sturdivant, A., Tridandapani, S. & Sotoudeh, H. (2023). Artificial Intelligence Literacy: Developing a Multi-institutional Infrastructure for AI Education. Academic Radiology, 30(7), 1472-1480. https://doi.org/10.1016/j.acra.2022.10.002
  • Shih, P., Lin, C., Wu, L.Y., & Yu, C. (2021). Learning Ethics in AI—Teaching Non-Engineering Undergraduates through Situated Learning. Sustainability, 13(7), 3718. https://doi.org/10.3390/su13073718
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