Why Generative AI Literacy, Why Now and Why it Matters in the Educational Landscape? Kings, Queens and GenAI Dragons

Introduction: Dracarys

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

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 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

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;

To Literate T[AI]hyself, know what, know how and know why : 3wAI as an emerging framework

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?

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?

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.

Data Accessibility Statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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).