Content uploaded by Aras Bozkurt
Author content
All content in this area was uploaded by Aras Bozkurt on Aug 29, 2024
Content may be subject to copyright.
EDITORIAL
CORRESPONDING AUTHOR:
Aras Bozkurt
Anadolu University, Türkiye;
Western Caspian University,
Azerbaijan
arasbozkurt@gmail.com
KEYWORDS:
Generative AI; GenAI; artificial
intelligence; AIEd; AI literacy;
AI competency; AI fluency; AI
Skills; AI readiness; education;
teaching; learning; higher
education; tertiary education;
K-12
TO CITE THIS ARTICLE:
Bozkurt, A. (2024). Why
Generative AI Literacy, Why
Now and Why it Matters in the
Educational Landscape? Kings,
Queens and GenAI Dragons.
Open Praxis, 16(3), pp. 283–290.
https://doi.org/10.55982/
openpraxis.16.3.739
Why Generative AI Literacy,
Why Now and Why it
Matters in the Educational
Landscape? Kings, Queens
and GenAI Dragons
ARAS BOZKURT
ABSTRACT
The rapid emergence of Generative Artificial Intelligence (Generative AI) has
fundamentally transformed the educational landscape, presenting both profound
opportunities and significant challenges. As a powerful and evolving digital creature,
generative AI requires a literacy (GenAI literacy) that goes beyond mere basic
understanding, requiring a comprehensive approach that integrates theoretical
knowledge, practical skills, and deep critical reflection. This paper argues that GenAI
literacy is crucial for surviving the complexities of human-machine interaction and
properly leveraging this technology, especially in educational settings. The proposed
3wAI Framework—encompassing the dimensions of Know What, Know How, and Know
Why—provides a structured yet adaptable model for cultivating GenAI literacy. Know
What focuses on the foundational knowledge of AI, including its definitions, capacities,
and decision-making processes. Know How emphasizes the practical application of AI,
guiding users in leveraging AI to solve problems, innovate, and drive positive societal
change. Know Why addresses the critical ethical and philosophical considerations,
urging users to prioritize responsible AI use, advocate for equity and social justice, and
critically assess the implications of AI technologies.
284Bozkurt
Open Praxis
DOI: 10.55982/
openpraxis.16.3.739
INTRODUCTION: DRACARYS
“I don’t know how to ride a dragon.” — Jon Snow
“Nobody does. Until they ride a dragon.” — Daenerys Targaryen
(from Game of Thrones)
Generative AI (GenAI) can be seen as a mythical creature, reminiscent of the dragons of ancient
tales, which has only recently hatched with the emergence of generative AI at the end of 2022.
This powerful digital creature, like a dragon, possesses a body forged from computer processors
and a soul woven from intricate algorithms. However, as with any newborn creature, we do not
yet know whether its nature will be good or bad. As a human-made technology, its character
and nature will be shaped not only by the features it possesses but also by the competences
and skills with which we use it. Thus, our concern should not only be with the potential power
of generative AI but also with the intentions and purposes of those who control it—those who
ride and guide this digital dragon.
From this perspective, the concept of GenAI literacy becomes utmost critical and crucial in
the broader context of human-machine interaction, particularly in the interaction between
humans and generative AI. GenAI literacy encompasses a set of competencies and skills that
determine how effectively, efficiently, competently, and responsibly the “kings and queens”—
the drivers of these dragons—will ride their digital creatures. Just as dragon fire, or Dracarys,
can be used to maintain peace or cause destruction, GenAI literacy involves being a critical and
responsible user or driver of this powerful digital creature.
Ultimately, unlike other technologies, generative AI represents a form of technology that can
learn, unlearn, and relearn. Its algorithms will be shaped by observing and imitating human
behavior—by learning how we, the dragon riders, think and act. Therefore, GenAI literacy is
not merely a concept to be defined but should be regarded as a living, evolving notion that
continually updates itself in response to new developments.
CURRENT STATE OF THE ART IN THE GENERATIVE AI LANDSCAPE
“I believe in everything until it’s disproved. So I believe in fairies, the myths, and
dragons. It all exists, even if it’s in your mind” — John Lennon
The pervasive integration of both generic AI and generative AI into our daily lives has
highlighted the need for effective utilization of these technologies (Chiu et al., 2024; Kong et
al., 2024; Laupichler et al., 2022; Sperling et al., 2024; Tlili et al., 2023). The way we integrate
these technologies into our lives, and the incidence and intensity with which we use them, has
proven the significance of GenAI literacy (Bozkurt, 2023a, 2023b; Bozkurt & Bae, 2024; Bozkurt
& Sharma, 2024; Casal-Otero et al., 2023; Haesol & Bozkurt, 2024; Ng et al., 2021a; Shiri, 2024;
Su et al., 2023). To cultivate responsible citizens who can use AI in a reliable, trustworthy, and
fair manner, it is essential to broaden participation in AI across all demographics and ensure
inclusive AI learning designs (Ng et al., 2021b). Rather than merely being consumers of this
technology, it is crucial to encourage users to engage with it thoughtfully and critically (Gupta
et al., 2024). Eventually, AI literacy is increasingly necessary in our technology-dominated era.
Some recent studies have examined the current state of AI literacy. Laupichler et al. (2022), for
instance, reported that while there has been increasing attention on AI literacy since the second
decade of the 2000s, research in this area remains in its infancy and requires further refinement,
particularly in defining AI literacy for adult education and determining appropriate content for
non-experts. In a systematic review, Almatrafi et al. (2024) categorized the literature on AI
literacy into three broad themes: conceptualizing AI literacy, promoting AI literacy efforts, and
developing AI literacy assessment instruments. Their review also highlighted that AI literacy
initiatives target a diverse range of populations, from pre-K students to adults in the workforce.
Additionally, in a comprehensive analysis, Stolpe and Hallström (2024) identified that AI literacy
frameworks are aligned with three distinct paradigms of technological knowledge: technical
competencies, technological scientific comprehension, and socio-ethical technical awareness.
Their findings suggest that AI literacy within the context of technology education emphasizes
technological scientific comprehension—such as understanding the nature of AI, recognizing
285Bozkurt
Open Praxis
DOI: 10.55982/
openpraxis.16.3.739
AI systems, and engaging in systems thinking—along with socio-ethical technical awareness,
including considerations of AI ethics and the human role in AI.
In brief, the integration of both generic and generative AI into everyday life has illuminated the
critical need for effective utilization and understanding of these technologies. As generative AI
becomes increasingly pervasive, its role in enhancing AI literacy is paramount, necessitating
the development of educational frameworks that not only elucidate AI functionalities but also
encourage critical engagement with the societal implications of these technologies. Given that
generative AI technologies are becoming ubiquitous in our lives, prioritizing AI literacy will be
essential in preparing individuals to engage with and shape the evolving technological and
educational landscape.
ON DEFINING AI LITERACY
“He who fights too long against dragons becomes a dragon himself; and if you gaze
too long into the abyss, the abyss will gaze into you.”— Friedrich Nietzsche
AI literacy is indeed a relatively new concept, loosely and inconsistently used, and there is no
agreed definition. The term was first introduced by Burgsteiner et al. (2016) and Kandlhofer et
al. (2016) to help understand the fundamental knowledge and concepts underlying AI-driven
technologies. Long and Magerko (2020, p. 2) further 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.” Wang et al.
(2023, p.3) defined AI literacy as “the ability to be aware of and comprehend AI technology in
practical applications; to be able to apply and exploit AI technology for accomplishing tasks
proficiently; and to be able to analyze, select, and critically evaluate the data and information
provided by AI, while fostering awareness of one’s own personal responsibilities and respect
for reciprocal rights and obligations”. Ng et al. (2021a; 2021b) proposed four key aspects of
AI literacy: knowing and understanding, using and applying, evaluating and creating, and
being aware of ethical issues. Building on this, Almatrafi et al. (2024) expanded this framework
to include six key aspects: recognizing, knowing and understanding, using and applying,
evaluating, creating, and navigating ethically.
While the literature on AI education often uses the terms AI literacy and AI competency
interchangeably, Chiu et al. (2024) made a distinction between the two. They posited that
AI literacy refers to an individual’s capacity to explain the operational mechanisms of AI
technologies and their societal implications, along with the ability to use these technologies
ethically and responsibly and to engage in effective communication and collaboration in diverse
contexts. This concept emphasizes the necessity of possessing knowledge and skills. Conversely,
AI competency is characterized as an individual’s confidence and aptitude in articulating the
functionalities of AI technologies, understanding their societal impact, utilizing them ethically,
and effectively communicating and collaborating across various environments. This concept
emphasizes the need for self-assurance and the ability to reflect on one’s understanding of AI
to facilitate ongoing learning, focusing on the proficiency with which individuals leverage AI for
positive outcomes.
In all, these definitions and conceptual frameworks share overlapping aspects while also
presenting divergent points. AI literacy actually makes it easier to make a definition by reducing
the competencies and skills expected from individuals to a specific technological field. However,
generative AI is a very rapidly developing and evolving technology and the ability of this
technology to learn, communicate and interact requires more caution when defining AI literacy.
In the context of this study, AI literacy, with a specific focus on GenAI technologies, based on
the principles of know what, know how and know why, is defined as such. Accordingly;
AI literacy is the comprehensive set of competencies, skills, and fluency required to
understand, apply, and critically evaluate AI technologies, involving a flexible approach
that includes foundational knowledge (Know What), practical skills for effective real-
world applications (Know How), and a deep understanding of the ethical and societal
implications (Know Why), enabling individuals to engage with AI technologies in a
responsible, informed, ethical, and impactful manner.
286Bozkurt
Open Praxis
DOI: 10.55982/
openpraxis.16.3.739
TO LITERATE T[AI]HYSELF, KNOW WHAT, KNOW HOW AND
KNOW WHY : 3wAI AS AN EMERGING FRAMEWORK
“A dragon without its rider is a tragedy. A rider without their dragon is dead.”
— Rebecca Yarros, Fourth Wing
Given that generative AI is a rapidly developing and evolving technology, its capacity to learn,
communicate, and interact necessitates a more cautious approach when defining AI literacy.
Additionally, from the perspective of individuals, it should be noted that each user may need
different degrees of AI literacy, from basic to advanced, and therefore the perceived degree of
AI literacy may be sufficient in many cases. This situation actually points out that AI literacy is
not a coded, fixed and unchanging concept, but a flexible, liquid and rapidly updatable concept.
Again, to recap metaphorically, if generative AI is a dragon, it is born, grows, develops, and
transforms. Thus, the dragon rider must adapt to this process, evolve alongside the dragon,
strive to understand it in all its facets, and learn, unlearn and relearn.
From this perspective, this study presents the 3wAI Framework for GenAI literacy. Unlike
previous AI literacy definitions, approaches or frameworks, it offers an adaptive and flexible
understanding according to one’s need to experience generative AI. To achieve this, it points to
different competences or skills in the three main dimensions and provides adaptable statements
with guiding questions. The answers given by the generative AI users to the questions asked
in the context of the 3wAI Framework allow them to understand how literate they are and
to make self-assessment. Again, the examination of competences and skills with questions
allows the 3wAI Framework, from individual to institutional level, that can be easily adapted to
different cultural and socio-economic contexts.
Besides, the fact that it consists of adaptable statements with guiding questions under the three
basic dimensions provides flexibility to the 3wAI Framework by allowing the answers to change
according to the rapidly developing and evolving generative AI technologies, or it allows us to
ask new questions to understand our competencies or skills according to the situations that
may arise with the possible capacity increase that we cannot predict from where we are today.
The 3wAI literacy framework argues that to achieve an effective and efficient generative AI
experience in a responsible, informed and in a conscious manner, it’s essential to critically
address the key questions within each dimension.
Know What (Knowledge-Related Dimension: Theoretical and Conceptual Aspects):
1. Define AI: What is the definition of artificial intelligence, and what are its core
components and overarching goals?
2. Understand AI Technologies: How do AI technologies function, and what are their
potential applications across various domains?
3. Differentiate AI Models: What are the key differences between various AI models, and
how do their functionalities and use cases differ?
4. Explain AI Decision-Making: How do AI models make decisions, and what processes and
algorithms guide their outputs?
5. Explain AI Learning Process: How do AI systems learn from data, and what roles do
machine learning and deep learning algorithms play in shaping AI behavior?
6. Assess AI Capacity: What are the capacities and limitations of AI systems, and where do
they succeed or face challenges?
7. Reflect on AI Features: What are the strengths, weaknesses, and potential biases of AI
technologies?
8. Recognize Synthetic Content: How can one identify and differentiate synthetic content
created by AI from human-generated content?
287Bozkurt
Open Praxis
DOI: 10.55982/
openpraxis.16.3.739
9. Evaluate Data Processing Impact and AI-Generated Content: How do data processing
methods impact the results generated by AI, and what biases might be introduced during
data handling?
10. Acknowledge Human Roles in AI Development: What is the importance of human input in
the development and fine-tuning of AI technologies?
Know How (Application-Related Dimension: Practical and Operational Aspects)
1. Leverage AI to Improve Society: How can AI technologies be used to address societal
challenges like enhancing healthcare, improving education, and promoting environmental
sustainability?
2. Collaborate with AI for Enhanced Effectiveness, Efficiency, and Productivity: How can AI
tools and systems be used to boost productivity, streamline workflows, and achieve more
effective outcomes in collaborative settings?
3. Master Prompt Engineering for Effective Communication: How can prompt engineering
be utilized to effectively communicate with AI systems and achieve desired outputs,
ensuring that instructions are clear and tailored to produce optimal results?
4. Evaluate AI Output Validity and Reliability: How can one assess the accuracy and
dependability of AI-generated outputs in decision-making processes?
5. Adapt AI Scenarios to Various Contexts: How can AI be effectively applied across different
domains such as education, business, and public services?
6. Customize Open-Source AI Models: What are the steps involved in modifying and refining
open-source AI models to meet specific needs while ensuring ethical compliance?
7. Develop AI-Driven Solutions: How can innovative, AI-powered solutions be created to
address specific challenges in various fields?
8. Analyze and Compare AI Technologies: How can different AI technologies be
systematically compared to evaluate their strengths, weaknesses, and utility?
9. Innovate New Approaches Using AI: What new methods and strategies can be developed
using AI to drive innovation and maintain a competitive edge?
10. Implement AI for Positive Transformation: How can AI technologies be transferred and
applied in new areas to drive positive change in industries, communities, and global
initiatives?
Know Why (Critical Perspective Dimension: Ethical, Epistemological and Ontological Aspects)
1. Prioritize Responsible AI Use: Why is it important to ensure that AI technologies are used
responsibly, and what are the societal impacts to consider?
2. Advocate for Ethical AI Practices: What ethical principles should guide the development,
deployment, and use of AI?
3. Exercise Caution in Sharing Personal Data: Why is it crucial to protect personal and
sensitive data when interacting with AI systems?
4. Defend Human-Centered Approaches: Why should AI systems prioritize human values
and needs, and how can technology serve humanity effectively?
5. Value Transparency in AI: Why is transparency in AI processes important, and how can
decision-making algorithms be made more understandable and explainable?
6. Stand Up for Equity and Social Justice: How can AI technologies be integrated to promote
equity and social justice, and avoid exacerbating inequality?
7. Criticize Biased AI: What methods can be used to identify and challenge biases in AI
algorithms to ensure fairness and inclusivity?
8. Reframe AI for Sustainability: How can AI technologies be developed and used to
contribute to sustainability and long-term societal well-being?
9. Identify Potential AI Risks: What are the potential risks associated with AI technologies,
and how can they be assessed and mitigated?
288Bozkurt
Open Praxis
DOI: 10.55982/
openpraxis.16.3.739
10. Imagine Alternative Speculative Future Scenarios: What are possible future scenarios
shaped by AI, and how can they inform current practices?
11. Evaluate AI-Based Outcomes: How can AI-generated results be critically assessed to
ensure alignment with ethical standards and societal goals?
12. Reflect on Human-AI Interaction: What are the implications of human-AI interaction, and
how do these relationships shape our understanding of technology and ourselves?
13. Defend Human Oversight and Role in Critical AI-Based Decisions: Why is it necessary to
ensure human involvement in critical decision-making processes where AI is used?
14. Acknowledge AI as a Human-Made Technology: Why is it important to recognize AI as a
creation of human ingenuity, with its inherent benefits and limitations?
15. Critically Assess Core AI Concepts: How should foundational concepts such as “artificial,”
“intelligence,” and “human” be questioned and analyzed for their implications and
limitations?
CONCLUSION
“If the sky could dream, it would dream of dragons. — Llona Andrews”
As manifested in the title of this paper, Why Generative AI Literacy, Why Now and Why it Matters
in the Educational Landscape?, the urgency of GenAI literacy in education cannot be overstated.
The rapid emergence of generative AI has fundamentally transformed the landscape of
teaching and learning, demanding that educators and learners alike develop a deep, critical
understanding of these technologies. This is not just about keeping pace with technological
advancements, but about shaping the future of education itself. GenAI literacy equips us to
harness these tools effectively, ensuring that they enhance rather than undermine educational
equity, creativity, and critical thinking. Now is the time to embed this literacy into our educational
systems, preparing the next generation of “kings and queens” not just to ride the generative
AI dragon, but to lead it with insight, responsibility, and a commitment to the collective good.
Briefly, navigating the landscape of generative AI requires a comprehensive literacy that
encompasses not only the theoretical or conceptual knowledge of AI (Know What), the practical
application of its tools (Know How), but also a deep ethical and philosophical understanding of its
implications (Know Why). Like a dragon, generative AI is a powerful digital creature with immense
potential for both creation and destruction. As we assume the role of the dragon riders, it is not
enough to merely understand or wield this power—we must critically engage with it, questioning
the very nature of the technology we have created, and guiding it with a strong ethical compass.
Such a scene implies that the stakes are high: unchecked, this dragon could amplify biases, erode
privacy, and deepen societal divides. But if harnessed wisely, with responsibility and foresight,
generative AI has the potential to reshape our world for the better, driving innovation, equity,
and sustainability. The challenge, therefore, before us is clear: to master the skills and knowledge
necessary to ride this dragon with both wisdom and integrity, ensuring that as it grows and
evolves, so too do we in our capacity to lead it toward a just and prosperous future.
ACKNOWLEDGEMENTS
With deepest gratitude, I bow to the master storyteller George R. R. Martin, who forged the
epic world of A Game of Thrones and breathed life into the legendary Daenerys Targaryen,
the Khaleesi, Mother of Dragons. Through Drogon, Rhaegal, and Viserion, Khaleesi’s fiery
companions, she ignited the flames of inspiration that burn brightly within this work.
To honor the brilliance of the mind that crafted such an epic tale, I leave this tribute in the
ancient tongue of High Valyrian: “Gaomagon drakarys syt se sy
-
z.”
DATA ACCESSIBILITY STATEMENT
Data sharing is not applicable to this article as no datasets were generated or analyzed during
the current study.
289Bozkurt
Open Praxis
DOI: 10.55982/
openpraxis.16.3.739
FUNDING INFORMATION
This paper is funded by Anadolu University with grant number YTS-2024-2559.
COMPETING INTERESTS
The author has no competing interests to declare.
AUTHOR CONTRIBUTIONS (CRediT)
Aras Bozkurt: Conceptualization, methodology, formal analysis, investigation, data curation,
writing—original draft preparation, writing—review and editing. The author has read and
agreed to the published version of the manuscript.
AUTHOR’S NOTE
This paper was proofread, edited, and refined with the assistance of OpenAI’s GPT-4o and
DeepL (Version as of August 1, 2024), complementing the human editorial process. The human
author critically assessed and validated the content to maintain academic rigor. The author also
assessed and addressed potential biases inherent in AI-generated content. The final version of
the paper is the sole responsibility of the human author (adapted from Bozkurt, 2024).
AUTHOR AFFILIATIONS
Aras Bozkurt orcid.org/0000-0002-4520-642X
Anadolu University, Türkiye & Western Caspian University, Azerbaijan
REFERENCES
Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs,
and implementation and assessment efforts (2019–2023). Computers and Education Open, 6,
100173. https://doi.org/10.1016/j.caeo.2024.100173
Bozkurt, A. (2023a). Generative artificial intelligence (AI) powered conversational educational agents:
The inevitable paradigm shift. Asian Journal of Distance Education, 18(1), 198–204. https://doi.
org/10.5281/zenodo.7716416
Bozkurt, A. (2023b). Unleashing the potential of generative AI, conversational agents and chatbots in
educational praxis: A systematic review and bibliometric analysis of GenAI in education. Open Praxis,
15(4), 261–270. https://doi.org/10.55982/openpraxis.15.4.609
Bozkurt, A. (2024). GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics and Integrity in a
Time of Generative AI. Open Praxis, 16(1), 1–10. https://doi.org/10.55982/openpraxis.16.1.654
Bozkurt, A., & Bae, H. (2024). May the Force Be With You JedAI: Balancing the Light and Dark Sides of
Generative AI in the Educational Landscape. Online Learning, 28(2), 1–6. https://doi.org/10.24059/olj.
v28i2.4563
Bozkurt, A., & Sharma, R. C. (2024). Are we facing an algorithmic renaissance or apocalypse? Generative
AI, ChatBots, and emerging human-machine interaction in the educational landscape. Asian Journal
of Distance Education, 19(1), i–xii. https://doi.org/10.5281/zenodo.10791959
Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016). IRobot: Teaching the Basics of Artificial
Intelligence in High Schools. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1).
https://doi.org/10.1609/aaai.v30i1.9864
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI
literacy in K-12: a systematic literature review. International Journal of STEM Education, 10(1). https://
doi.org/10.1186/s40594-023-00418-7
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and
competency? A comprehensive framework to support them. Computers and Education Open, 6,
100171. https://doi.org/10.1016/j.caeo.2024.100171
Gupta, A., Atef, Y., Mills, A., & Bali, M. (2024). Assistant, Parrot, or Colonizing Loudspeaker? ChatGPT
Metaphors for Developing Critical AI Literacies. Open Praxis, 16(1), 37–53. https://doi.org/10.55982/
openpraxis.16.1.631
Haesol, B., & Bozkurt, A. (2024). The untold story of training students with generative AI: Are we
preparing students for true learning or just personalization? Online Learning, 28(3). https://doi.
org/10.24059/olj.v28i3.4689
290Bozkurt
Open Praxis
DOI: 10.55982/
openpraxis.16.3.739
TO CITE THIS ARTICLE:
Bozkurt, A. (2024). Why
Generative AI Literacy, Why
Now and Why it Matters in the
Educational Landscape? Kings,
Queens and GenAI Dragons.
Open Praxis, 16(3), pp. 283–290.
https://doi.org/10.55982/
openpraxis.16.3.739
Submitted: 20 August 2024
Accepted: 21 August 2024
Published: 29 August 2024
COPYRIGHT:
© 2024 The Author(s). This is an
open-access article distributed
under the terms of the Creative
Commons Attribution 4.0
International License (CC-BY
4.0), which permits unrestricted
use, distribution, and
reproduction in any medium,
provided the original author
and source are credited. See
http://creativecommons.org/
licenses/by/4.0/.
Open Praxis is a peer-reviewed
open access journal published
by International Council for
Open and Distance Education.
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016, October). Artificial intelligence
and computer science in education: From kindergarten to university. 2016 IEEE frontiers in education
conference (FIE) (pp. 1–9). IEEE. https://doi.org/10.1109/fie.2016.7757570
Kong, S.-C., Korte, S.-M., Burton, S., Keskitalo, P., Turunen, T., Smith, D., Wang, L., Lee, J. C.-K., & Beaton,
M. C. (2024). Artificial Intelligence (AI) literacy - an argument for AI literacy in education. Innovations
in Education and Teaching International, 1–7. https://doi.org/10.1080/14703297.2024.2332744
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). What is AI Literacy? Competencies and Design Considerations.
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. https://doi.
org/10.1145/3313831.3376727
Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021a). AI Literacy: Definition, Teaching, Evaluation
and Ethical Issues. Proceedings of the Association for Information Science and Technology, 58(1),
504–509. https://doi.org/10.1002/pra2.487
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021b). Conceptualizing AI literacy: An exploratory
review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.
caeai.2021.100041
Shiri, A. (2024). Artificial intelligence literacy: a proposed faceted taxonomy. Digital Library Perspectives.
https://doi.org/10.1108/dlp-04-2024-0067
Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of
artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education
Open, 6, 100169. https://doi.org/10.1016/j.caeo.2024.100169
Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and
Education Open, 6, 100159. https://doi.org/10.1016/j.caeo.2024.100159
Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial Intelligence (AI) Literacy in Early Childhood Education:
The Challenges and Opportunities. Computers and Education: Artificial Intelligence, 4, 100124. https://
doi.org/10.1016/j.caeai.2023.100124
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What
if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart
Learning Environments, 10(1), 1–24. https://doi.org/10.1186/s40561-023-00237-x
Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence:
validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology,
42(9), 1324–1337. https://doi.org/10.1080/0144929x.2022.2072768
