What is 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.
Chan, CKY (2023)
Artificial Intelligence (AI) has revolutionized human capabilities by expanding communication and information acquisition. Not only does AI enable human-to-human communication as well as communication with machines, it also offers diverse modes of interaction, including text-based, graphical, and even coding-based responses. AI has now permeated various industries to improve work efficiency and create future job opportunities, yet how well the public understands AI technologies, and how researchers define AI literacy, both continue to be under-explored (Ng et al., 2021).
Existing literature has offered various definitions and conceptualisations of AI literacy. Long and Magerko (2020) defined AI literacy as “a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace” (p.2). Similarly, Kong et al. (2021b) proposed that AI literacy includes three components: AI concepts, using AI concepts for evaluation, and using AI concepts for understanding the real world through problem solving; they further added that training is needed to “enable people to use AI appropriately while successfully protecting their benefits and privacy” (p.1). By contrast, Cetindamar et al. (2022) sought to define AI literacy in the context of organizational and digital workplaces, proposing that it technology-related (e.g., one’s ability to collect and analyse data), human-machine-related (e.g., skills to leverage human-robot interactions), learning-related (e.g., skills to continue learning in face of a volatile future), and work-related (e.g., skills to effectively use AI in the workplace) capabilities.
Consolidating key components identified in the literature, AI literacy should include understanding and recognising AI’s capabilities and limitations, competencies to utilize AI effectively, and ethics and privacy-related issues in AI. With the rapid emergence and development of AI, fostering AI literacy has become essential, not only within academic contexts and the workplace but also within our daily lives. However, in doing so, it is also crucial to recognise the potential challenges of fostering AI literacy given the rapid development of AI technologies and applications, as well as the potential barriers posed by factors such as resource availability and accessibility constraints.
Are You an AI Literate Learner?
According to Chan (2023), an AI literate learner can:
Comprehend basic AI terminology and how AI systems function,
Utilize AI applications (e.g., chatbots and AI assistants),
Differentiate between realistic expectations of AI versus exaggerated hype,
Understand AI safety and security (e.g., awareness of potential risks, threats and misuse), and
Use AI responsibly (e.g., questioning reliability of AI-generated content and considering ethical implications).
*Note: The specific context(s) or role(s) that an individual is situated in may influence their understanding of AI literacy.
Are You an AI Literate Learner?
Why is AI literacy important?
Since AI is rapidly developing and increasingly incorporated into daily routines, it is important for individuals to understand AI and be able to effectively utilise AI technologies. AI literacy is important because it encourages learners to:
Identify misconceptions related to AI
Due to swift technological advancements, widespread rumours and exaggerations, and social media marketing strategies made to hook the attention of viewers, individuals may only have a superficial understanding or misconceptions about AI. For example, one common misconception is that AI methods work similarly to human brains; according to Emmert-Streib et al. (2020), this is not exactly true as the AI “neural network” models differ in structure and connectivity when compared to biological brains. Overall, understanding the concepts, applications, and realistic capabilities of AI is crucial for efficient, effective, and responsible interactions with such technologies and tools. Additionally, as individuals develop AI literacy, they will gain accurate understanding of AI concepts and utilisations.
Using AI ethically and responsibly
Understanding the dangers and ethical implications of AI ensures that privacy is safeguarded and unbiased decision-making is upheld. Debates have discussed the possibility that AI may eventually create an illusion of intelligence, where humans could be ‘deceived’ while interacting with technologies which they believe to be ‘intelligent’ (Natale, 2021). Having a solid ethical foundation in AI literacy is thus significant as it will serve as the basis for responsible AI development and safe implementation of AI tools and resources.
Enhancing career opportunities
Being AI literate will enable individuals to leverage these new technologies while also navigating around pitfalls, making them more competitive in the job market. There are now also AI literacy programmes in adult education that relate taught content to specific professional requirements or focus on a subject area as well (Laupichler et al., 2022). Following the COVID-19 pandemic, the need for AI literacy is further emphasised as organisations have shifted to remote work that utilise technology-dependent communication platforms. With the use of AI technologies and integration of AI assistants into an array of digital platforms, being AI literate is integral for maintaining competitiveness and creating new opportunities in both the job market and society.
Developing innovative and creative products
AI can augment human creativity and help us address challenges in innovation that would otherwise require entire companies to develop solutions for (Eapen et al., 2023). These developments, including AI-powered devices, predictive analytics, and health diagnostics now implemented across numerous industries, demonstrate how AI has the power to fundamentally change our daily lives. The emergence of generative AI, including technologies that allow the generation of texts, graphics and other forms of media, further emphasise the crucial role of AI in fostering creativity and driving innovation.
Opportunities and challenges in AI literacy
As AI technologies become increasingly prevalent, individuals who lack access to or are unable to utilise these technologies may be left behind. This could result in a widening divide between those who have the skills and resources to make the most of AI technologies, and those who do not, exacerbating effects of digital and technological poverty. These factors contribute to one’s resistance of, and create barriers to acquiring AI literacy, as it impacts the extent to which they engage with and learn about such tools in the first place. For example, students may be unfamiliar with the potentials and limitations of AI if they refuse to use it due their refusal to let AI overshadow human creativity (e.g., Chan & Hu, 2023), whereas teachers may be unconfident or unsure of how to teach AI literacy to a generation of digital natives who are far more used to technology and its trends (e.g., Chan & Lee, 2023). With the increasing implementation of AI into education, it is crucial to ensure equitable access to AI technologies and provide support for AI literacy education to address these challenges.
AI literacy may also lead to over-reliance on AI tools and less critical thinking from users. For example, Cardon et al. (2023) found that communication instructors perceived AI-assisted writing to be a challenge for critical thinking and authenticity in writing. Creative problem-solving could also eventually be replaced by AI technology, which hinders human creativity and thought. Another challenge is the amplification of misleading content. Training data for AI could be biased to create discriminatory results (Zuiderveen Borgesius, 2018), and unethical use of AI may lead to flawed or biased information and content, contributing to the spread of misinformation or increased sophistication of malicious activities and cyber threats. As Westerlund (2019) wrote, the current era is “characterized by digital disinformation and information warfare led by malevolent actors running false information campaigns to manipulate public opinion” (p.39), and that the ease of obtaining and spreading misinformation through social media makes it difficult to know what to trust, resulting in harmful consequences to the society.
It is crucial to implement measures to ensure that AI literacy promotes utilising AI responsibly and ethically to mitigate these risks.
Southworth et al. (2023) believed that by providing learners with AI learning opportunities, they can be empowered with the knowledge and skills to thrive with AI, while Lee and Park (2023) claimed that “AI can make humans more human by helping to explore what human spirit can do better than machines” (p.9). Despite concerns about AI dependence and job replacement, promoting AI literacy can foster critical thinking and problem-solving skills, providing individuals with the opportunity to develop their own strengths and character. By becoming AI literate, individuals can positively impact the workforce and the society, capitalising on the opportunities offered by AI (Kong et al., 2021b).
Whilst AI literacy necessitates continuous learning to keep up with the advancements of new technologies, appropriate utilisation of AI can enhance task efficiency that does not rely heavily on human cognition. Language boundaries are also quickly disappearing with AI-powered translation systems simplifying cross-cultural communications (e.g., Rehm et al, 2020), while AI-integrated intelligent robotics have been helping advance environmentalism and conservation (Dauvergne, 2020). In embracing AI literacy, humans can effectively utilise and evaluate AI while maintaining their autonomy and reasoning abilities, enabling them to identify effective solutions and gain realistic understandings of the world.
How is AI literacy developed?
Different technologies are used in educational contexts. Chan (2023) identified ten key areas directly relevant to planning AI policies in higher education institutions, which highlight important considerations for the use of AI in teaching and learning:
Understanding, identifying, and preventing academic misconduct and ethical dilemmas
Addressing governance of AI: data privacy, transparency, accountability, and security
Monitoring and evaluating AI implementation
Ensuring equity in access to AI technologies
Attributing AI technologies
Providing training and support for teachers, staff, and students in AI literacy
Rethinking assessments and examinations
Encouraging a balanced approach to AI adoption
Preparing students for the AI-driven workplace
Developing student holistic competencies/generic skills
Chan (2023) also emphasised that universities should “prioritize fostering critical thinking among students” (p.17) to embrace AI technologies and preparing students for the AI-driven workplace. These findings point to crucial considerations for developing AI literacy among individuals.
With the integration of AI into teaching and learning, research has explored different ways to cultivate AI literacy. In a literature review, Laupichler et al. (2022) found studies approaching this across the levels of individual courses to broader curricula; governments have also recognised the need to implement strategies and programmes to strengthen AI literacy among citizens. Below are two case studies that demonstrate approaches to developing students’ AI literacy, involving both establishing a basic understanding of AI technologies and applications, followed by hands-on experiences.
Remote learning and hands-on session
Perchik et al. (2023) conducted a study aimed at enhancing students' AI literacy, with a focus on participants from radiology programmes. The course was structured in two main parts. Firstly, remote learning lectures were delivered, covering basic AI terms and methods, clinical applications of AI in radiology, as well as special topics lectures on the economics of AI, ethics of AI, algorithm bias, and implications of AI in medicine. Following this, a hands-on clinical AI session was implemented, where participants could experience using an AI-assisted viewer and system for diagnosing advanced cancer. The course collected pre- and post-course evaluations that were scored by students, revealing a significant increase in students' AI knowledge after completing the course, as well as an increase in their interest in AI in radiology education. The course structure, which included remote learning and both theoretical and practical components, proved effective in developing students' AI literacy.
Shih et al. (2021) adopted a situated-learning-based design for an AI course to examine its effect on students’ understanding of AI, AI teamwork, and attitudes towards AI. The instructional design in the study involved several components. Firstly, a lecture on AI principles was delivered, using autonomous vehicles as an illustrative application to discuss the principles, goals, and potential ethical concerns. Authentic scenarios of AI application were also provided to students to demonstrate how specific goals (tasks) were achieved with AI tools. The instructor then demonstrated how to make an AI model to recognise objects and perform actions accordingly; students were subsequently then introduced to an online platform and proceeded to train an AI model to make a car kit that could achieve the given goal, with the help of an instructor. Finally, instructors would evaluate students’ achievements based on the performance of their trained model. Pre- and post-course evaluations revealed that students' understanding of AI significantly improved after completing the course. The hands-on activities and group work elements of the course enhanced students' perceptions of AI issues. Moreover, by incorporating AI technology applications in daily life, the course design helped students develop a greater awareness of ethical issues surrounding AI.
How Should I Assess AI Literacy?
AI literacy is a rapidly developing field, and consequentially, assessment of AI literacy can be complex and diverse. For instance, it is influenced by various factors, including the specific AI technologies being used, learning contexts, institutional regulations, policies, and resource limitations. Additionally, assessments may vary depending on the desired learning outcomes and objectives of the respective AI literacy courses or programmes. In recent years, studies have offered distinct approaches to assess or evaluate AI literacy courses and programmes.
Test and surveys
To investigate students’ development of AI literacy, Kong et al. (2021a) designed an AI Concepts test as well as self-report surveys on AI Literacy and AI Empowerment, all implemented in a non-discipline specific AI literacy course for higher education students.
The AI Concepts consisted of 14 multiple choice questions, each with 4 answer options, which were related to content covered in the course and were specially designed, based on Bloom’s Taxonomy, as direct measurements of students’ progress and mastery in understanding AI concepts. The AI Literacy Survey included 10 items on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). It measured participants’ personal perceptions of their: (1) AI literacy level, (2) mastery of AI concepts, and (3) their understanding of the real world given what they learned throughout the course. The AI Empowerment Survey consisted of 17 items, also on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree), measuring how the extent to which students feel empowered with their use of AI, for example, how AI has impacted them, how it has motivated them, and how it has made their lives more meaningful. All three measures (the AI Concepts test, and the AI Literacy and Empowerment surveys) were administered before and after the course. The tests and surveys assessed participants and revealed students’ development pre- and post-course.
In an AI literacy programme for university students, Kong et al. (2022) utilised reflective writing as one of the main measures of evaluating students’ AI literacy achievements. The task focused on evaluating students’ understanding of AI and ethics, where students were asked to write 100 - 200 words in English or Chinese before and after each course throughout the programme, using the Moodle discussion forum. Results showed that students perceived flipped classrooms and project-based learning were effective classroom designs for developing their understandings of concepts and ethical awareness relevant to AI. Samples from students’ written work showed them reflecting on how they were able to gain a better understanding of what AI is, their increased awareness of ethical considerations such as privacy and fairness, and their recounts of applying their newfound AI knowledge to address problems in daily life.
In the AI-era, cultivating AI literacy is crucial. It equips individuals with the understanding and skills needed to harness the potential of artificial intelligence safely and responsibly. By embracing AI literacy, we empower ourselves to navigate, innovate, and shape a world where human creativity and technologies communicate and collaborate for the benefit of all.
Cardon, P., Fleischmann, C., Aritz, J., Logemann, M., & Heidewald, J. (2023). The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age. Business and Professional Communication Quarterly, 86(3), 257-295. https://doi.org/10.1177/23294906231176517
Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2022). Explicating AI Literacy of Employees at Digital Workplaces. In IEEE Transactions on Engineering Management (pp.1-14). IEEE. https://doi.org/10.1109/TEM.2021.3138503.
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., & Hu, W. (2023). Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education. International Journal of Educational Technology in Higher Education. [in press]
Chan, C.K.Y., & Lee, K.K.W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and Millennial Generation teachers? https://arxiv.org/abs/2305.02878
Dauvergne, P. (2021). The globalization of artificial intelligence: consequences for the politics of environmentalism. Globalizations, 18(2), 285-299. https://doi.org/10.1080/14747731.2020.1785670
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
Emmert-Streib, F., Yli-Harja, O., & Dehmer, M. (2020). Artificial Intelligence: A Clarification of Misconceptions, Myths and Desired Status. Front. Artif. Intell., 3, 524339. https://doi.org/10.3389/frai.2020.524339
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
Lee, J., & Park, J. (2023). AI as “Another I”: Journey map of working with artificial intelligence from AI phobia to AI-preparedness. Organizational Dynamics, 52, 100994. https://doi.org/10.1016/j.orgdyn.2023.100994
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
Natale, S. (2021). Deceitful media: Artificial intelligence and social life after the Turing test. Oxford University Press, USA.
Ng, D.T.K., Leung, J.K.L., Chu, S.K.W., & Qiao, M.S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
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),
Rehm, G., Marheinecke, K., Hegele, S., Piperidis, S., Bontcheva, K., Hajič, J., Choukri, K., Vasiljevs, A., Backfried, G., Prinz, C., Pérez, J.M.G., Meertens, L., Lukowicz, P., van Genabith, J., Lösch, A., Slusallek, P., Irgens, M., Gatellier, P., Köhler, J., Le Bars, L., ... Yvon, F. (2020). The European language technology landscape in 2020: Language-centric and human-centric AI for cross-cultural communication in multilingual Europe. arXiv:2003.13833. https://doi.org/10.48550/arXiv.2003.1383
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
Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/j.caeai.2023.100127
Westerlund, M. (2019). The Emergence of Deepfake Technology: A Review. Technology Innovation Management Review, 9(11), 39-52.
Zuiderveen Borgesius, F. (2018). Discrimination, artificial intelligence, and algorithmic decision-making. Council of Europe, Directorate General of Democracy. Retrieved 7 September, 2023, from https://rm.coe.int/discrimination-artificial-intelligence-and-algorithmic-decisionmaking/1680925d73