AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices
A Rapid Response Paper for MIT Open Learning
by Angela Gunder, Joshua Herron, Nicole Weber, Colette Chelf, and Sherry Birdwell
Published onNov 26, 2024
AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices
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Author Note: Correspondence concerning this article should be addressed to Angela Gunder: [email protected]
Abstract
In an age of significant academic transformation due to the vector of artificial intelligence (AI), this study explores the intersection of AI literacies and open educational practices (OEP) in fostering an “opened culture” across learning environments and communities. The authors situate AI literacies as a set of interconnected competencies transcending technology use with a propensity for advancing the goals of open pedagogy. Inspired by the work of Stuart Hall and Douglas Belshaw, this qualitative study presents insights from 34 educators from around the world, surfacing the impact of AI on how open educational practitioners are collaborating, creating OER, and building connections to the communities that they serve. This research advances the burgeoning discourse on AI in open education, illuminating new pathways for empiricism and advocacy as the field collectively reimagines a more open and inclusive future of learning.
Keywords: AI literacies, opened culture, generative AI, artificial intelligence, open educational practices, open pedagogy, open educational resources, OEP, OER
Introduction
The rapid evolution and integration of generative artificial intelligence (AI) is reshaping education, creating challenges and opportunities for teaching and learning. AI’s potential to enhance learning is undeniable, with countless educators exploring functions and use cases that offer the possibility of more equitably advancing access to education. In particular, AI literacies, which are a collection of skills and knowledge that a person needs to understand, use, and critically evaluate AI, have become increasingly important. Beyond mere tool usage, AI literacies are rooted in cultural and contextual awareness, empowering educators and learners to critically engage with AI in meaningful ways (Bali, 2024a; Bali, 2024b; Gupta, A., et al., 2024). This study situates AI literacies above AI tools themselves as central to fostering cultures of openness in education globally. Through the humanized tenets of open pedagogy (Bali et al., 2020a; Bali et al., 2020b; DeRosa & Jhangiani, 2017; DeRosa & Robinson, 2017; Hegarty, 2015), AI literacies help create learning spaces that are inclusive, culturally affirming, and responsive to the needs of diverse communities.
While the promise of AI’s alignment to the goals of the open community remains a topic of discussion amongst educators (Bozkurt, 2023a; Ossiannilsson et al., 2024; UNESCO, 2024; Xiao, 2024), practices that actualize this promise of openness through the personalization of content (Gupta, G., 2023), collaboration, and streamlined resource creation (Baijnath, n.d.) are still nascent. This study examines the relationship between the practices utilized to advance open pedagogy in an educational age informed by AI, exploring the intersection of AI literacies and the advancement of “opened culture,” defined as a vibrant, active, and enduring ecosystem that gives meaning to openness and educational access through collaboration, creation, and connection. In dialogue with thought leaders from around the globe, this inquiry presents perspectives on how AI literacies and open pedagogy might be aligned to support the values and objectives of the open community in establishing education as a human right (Cape Town Open Education Declaration, 2024; UNESCO, 2012; UNESCO, 2019).
Guided by the following research questions, this study investigates global educators’ perspectives on AI literacies development and the connections to open educational practices (OEP):
What are the perspectives of global educators on the usage of AI in open education?
What technological, pedagogical, or content literacies are required to utilize AI to support open educational practices?
How do AI literacies and practices give meaning to openness and educational access through collaboration, the creation of OER, and building connections to community?
This exploration was commissioned by UNESCO IITE and Shanghai Open University as part of their ongoing commitment to surfacing high-impact practices for advancement of the UN Sustainable Development Goals (SDGs)(UNESCO, 2017; Global Goals, n.d.). It specifically examines how AI impacts open collaboration, the creation of open educational resources (OER), and the alignment of resources and initiatives to the needs and values of the open community.
Literature Review
Unpacking the complexities of AI’s impact in open education begins with an examination of the literature, particularly empirical findings and conceptual frameworks that explore the challenges of, and subsequently, the opportunities for integrating AI into open education in ways that are supportive of an opened culture.
Global Perspectives on AI and Open Education
While the proliferation of analyses on generative AI in education serves as a marker of the burgeoning discussion in the field (Bozkurt, 2023b; Castillo-Martínez et al., 2024; Davis, 2023; Mhlanga, 2023), empirical studies looking at the intersection of AI and open education are only starting to appear within the literature (Cox et al., 2024; van den Berg, 2024; van Wyk et al., 2023). While existing research explores educators' attitudes toward AI across various regions, there is a lack of comprehensive, cross-regional comparisons that account for the nuanced perspectives of educators from underrepresented areas, making it essential to investigate further how contextual factors shape global views on AI in education (Almasri, 2024; D. Lee et al., 2024; Maznev et al., 2024; Mustopa et al., 2024; Smolanksky et al., 2023). Concerns around authorship and attribution within AI outputs abound, particularly within the realm of open education’s culture of open licensing and transparency of authorship (Bozkurt, 2024; Lalonde, 2023; Stacey, 2023). Additionally, while some global regions are reflecting on AI as a tool to increase engagement in OEP (Baijnath, n.d.), others have identified infrastructure and internet access constraints in applying AI technology, as well as challenges with creativity loss and misuse of AI outputs (Mustopa et al., 2024; Smolansky et al., 2023).
AI Literacies and Open Pedagogy
The field has seen a proliferation of frameworks and resources created specifically to govern and guide AI usage in education (Cardona et al., 2023; UNESCO, 2024, September 26; Georgieva et al., 2024; Hibbert et al., 2024; Laupichler et al., 2022; K. Lee et al., 2024; MacCallum et al., 2024; Miao & Cukurova, 2024; Miao & Shiohira, 2024; Ng et al., 2023; World Economic Forum, 2022). However, little data exists on the specific competencies and knowledge domains that users need to effectively leverage AI for open education. Furthermore, many frameworks present literacy as a binary state of literacy versus illiteracy, rather than as a plurality of interconnected and constantly evolving socioculturally-situated practices (Gee, 1989, 2017; Heath, 1983; Lankshear & Knobel, 2006, 2008; Street, 1984, 2014). As literacies frameworks continue to appear to help shape educators’ understanding of academic uses of AI (Ng et al., 2021), there are several established frameworks from non-AI specific domains of learning that prove impactful for considering AI’s role in advancing open pedagogy (Belshaw, 2014, 2023; Bozkurt, 2023a; Chan & Lee, 2023; Kamalov et al., 2023; Mishra & Koehler, 2006; Mishra, 2019; Zawacki-Richter et al., 2019). Furthermore, connecting AI literacies and OEP requires sensitivity to power dynamics and agency in collaborative digital spaces (Mills, K.A., 2010; Gee, 1999, Belshaw, 2016), calling for further examination of how AI is changing the way that open communities work together.
AI and Advancing Opened Culture
Stuart Hall’s conceptual framing of culture (1997) illuminates a path for understanding how opened culture is comprehensive of the people that collaborate on open education, the objects (or OER) that they create, and the events that take place as they connect to their community. As researchers begin to explore the impact of AI on OEP (Aksoy & Kursun, 2024; Baijnath, n.d.; Mills et al., 2023), there is a need to look beyond the functionality of AI tools and toward the ways that AI is changing the ways that we work together. While early theoretical framings of AI in open education highlight the ways that generative AI tools might offer potential benefits for open education (Bozkurt, 2023a; Kamalov et al., 2023; Ossiannilsson et al., 2024; van Wyk et al., 2023; Xiao, 2024), empirical data is lacking on the specific types of OER that educators are producing with AI. Additionally, comprehensive research on the ethical considerations of using AI in open education is needed, particularly concerning issues of the environmental impacts of AI, data privacy, open licensing and attribution, accuracy of cultural affirmation, and equitable access to AI technologies (Bozkurt, 2023a; Ossiannilsson et al., 2024; Santarius et al., 2023; Mills et al., 2023).
Methodology
To explore the intersection of AI literacies and open educational practices (OEP), the research team conducted a qualitative study, using portraiture methodology to center impactful practices over pathology (Lawrence-Lightfoot & Davis, 1997; Lawrence-Lightfoot, 2005). Working under the guidance of UNESCO IITE as a part of their larger work on AI in education, this study was conducted to achieve two interconnected goals: 1) to develop a resource for employing AI literacies to foster openness in education, and 2) to highlight educators' perspectives and practices worldwide, uncovering the challenges and opportunities advanced by open educational practitioners in an AI-enabled world. Thirty-four educators (see Appendix C: Acknowledged Scholars) from diverse global regions, including Europe, North America, Asia, Africa, and Latin America were selected using a purposeful sampling strategy (Suri, 2011; Palinkas et al., 2015), with additional participants identified through snowball sampling (Goodman, 1961).
Interviews conducted in Zoom were guided by a semi-structured protocol (see Appendix A: Interview Protocol). All participants provided informed consent which noted their content would be attributed to them and not anonymized to align with the community goals of UNESCO IITE. Complementary survey data (see Appendix B: Survey Protocol) from respondents provided a broader context and helped triangulate interview findings. Data were initially coded in Taguette1 using a ground theory approach (Glaser & Strauss, 2017; Strauss & Corbin, 1997). Additionally, a priori codes were created from the Dimensions of AI Literacies (Gunder et al., 2024), remixed from Belshaw’s work on digital literacies (2014). The researchers also engaged ChatGPT 4o in the iterative coding process by generating summaries of transcripts, which were then compared to the human-coded findings. Participants reviewed and validated their interview transcripts to ensure accuracy and contextual alignment, and performed member checks on the final content.
Findings
Gleaned from discussions with 34 educators, the findings presented here are a brief synthesis of the key findings from this study—case examples and a detailed analysis report can be accessed from UNESCO IITE at [insert link once published].
AI and Open Education: Challenges and Opportunities
While AI is promising in its potential to support open pedagogy, participants identified various pitfalls needing mitigation. They cited the need for critical literacies in assessing the outputs of AI and concerns about AI’s exacerbation of biases and harmful representations of cultures, as well as protecting open licenses and attribution. Several educators cited their growing concerns around recent discoveries of the environmental impact of the high energy consumption required by AI technologies, particularly in disproportionately affecting marginalized communities. Additionally, they brought up nascent concerns about the efficiency created by AI potentially replacing jobs in education, encouraging educators to be less open with their data and practices such that they might protect job security for their colleagues. Ultimately, several educators expressed concerns that while AI might advance UN SDG #4: Quality Education, it might simultaneously exacerbate challenges in meeting several other SDGs related to equity, well-being, and societal good.
In terms of opportunities, the educators most prominently cited the multilingual support of AI in open education across a spectrum of use cases, particularly in opening pathways to international collaborations that wouldn’t exist otherwise. Participants also saw the use of AI tools directly with their learners as a way to open their teaching practices, fostering critical thinking and ethical awareness. They also shared their methods for customizing and safeguarding the outputs of AI, using tools like Notebook LM2 to train AI on “walled gardens” of data that they could validate. While they cited that AI could be used for content creation and personalization, they stressed the need to assess content for accuracy, pointing to efficiency loss due to a growing overreliance on AI tools. Most notably, the educators cited the greatest opportunity for AI in open education as the ability to advance open pedagogy as a critical competency, explaining how open pedagogy could help to guide strategy for AI’s usage across all contexts.
AI Literacies and Open Pedagogy
In looking at the requisite competencies and skills needed to impactfully and ethically use AI for open education, the educators overwhelmingly pointed to the need to have mature knowledge of technology, content, and pedagogy. They cited a need for both educators and learners to know how AI tools work and where they pull data from, as well as the mechanics for prompting AI systems. Beyond technology, they saw content knowledge as the most critical element for successfully using AI in open education, citing AI’s inability to replace disciplinary expertise. This illuminated a point of tension for student usage of AI tools without having the subject matter expertise to validate content. Notably, the librarians interviewed all shared the importance of their historical work advancing information literacies as critical in this age of AI. Lastly, the educators affirmed that an understanding of the learning process was necessary in using AI for open education, particularly in the creation of humanized, learner-centered content. They shared the need for mature teaching philosophies and grounded values before using AI in open education, citing instances where their dedication to culturally-affirming teaching and trauma-informed teaching were key to their process of using AI in conjunction with OEP.
In terms of practices related to AI and open education, the AI literacies most prominently mentioned were critical AI literacies and the examination of the power dynamics and ethical considerations inherent in AI practices. They also cited constructive AI literacies as important in this formative time for AI and open education for understanding how AI tools could be used to generate open content in ethical ways. Furthermore, where other conversations with educators on AI in education were basic in their expression of civic AI literacies, the open educational practitioners who took part in this study expressed deep knowledge of the potential of AI and OEP to be used to positively contribute to their local and global communities. They shared how their use of open pedagogy as a touchstone allowed them to better understand both the challenges and opportunities for AI in open education as a vector for community empowerment, engagement, and societal progress.
AI and Advancing Opened Culture
Lastly, the educators shared their insights on how AI was affecting open education across three dimensions of culture—in open collaborations, in the creation of OER, and in the connections built to the community. While open education has long held well-developed practices for collaboration, they noted that the use of AI remains a human-to-AI point of engagement. The educators also candidly discussed that while there are numerous conversations about how AI could be used for the creation and remix of OER and other open knowledge products, they were not seeing many instances of this in practice. The scholars that took part in this study shared the opinion that while AI has the potential to create more open pathways for connections between educators and learners, there is a need to have well-formed and grounded values for open education before using AI, lest the efforts employed by educators do more harm than good.
Conclusion
Through these study findings, our field has an opportunity to explore the relationship between AI and open education as critical to understanding AI’s impact on global communities of practice. Open pedagogy offers a powerful framework for integrating AI into education in ways that align with the values of open educators worldwide. Similar to the “open turn” (Gunder, 2021) that surfaced the shift in focus from objects (OER) to practices (OEP), to people (open pedagogy), situating AI as a vector to advance openness in education starts by looking beyond tools towards the practices and values embedded within and across communities. As affirmed by the findings of this study, it’s the people—the practitioners, educators, leaders, and most importantly, learners—that drive the future of AI and open education through their expression of interconnected AI literacies within their open educational practice. Our communal inquiry and advocacy at this intersection are essential for ensuring that AI aligns with the fundamental elements of an opened culture—collaboration, creation, and connection—to ultimately achieve the promise of open pedagogy.
AI Usage
The authors declare that generative AI was used to create transcripts of Zoom interviews and to generate summaries of transcript text from interview data (offered with informed consent) to compare to the human-coded data summaries prepared by the research team. Generative AI was not used for the writing of the manuscript nor in the creation of images, graphics, tables, or corresponding captions.
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Appendices
Appendix A: Survey Questions
You are being asked to participate in a research study for UNESCO IITE on AI in higher/open education. Your participation in this research study is voluntary and you do not have to participate. Your reflections provided here will be attributed to you as a means of building connections and community with the field around the perspectives that you share. Please only share information you are comfortable with being made public as a UNESCO IITE open knowledge product. Prior to publication, we will conduct member checks where you will be able to confirm or clarify your responses.
Feel free to ask questions before making your decision whether or not to participate. For questions or concerns about the study you may contact Dr. Angela Gunder by email at [email protected].
Thank you in advance for your time and insights.
Name:
Email address:
Occupation/Role:
Institution/Organization:
Years of Experience:
AI Usage Matrix:
Which of the following thematic areas would you be willing to share your perspectives on?
AI Literacies (Competencies and Skills) Only
AI Literacies + AI and Open Education
Would you be willing to participate in a follow-up Zoom interview if the researchers decide further data collection or clarification is needed?
Yes
No
What literacies practices and skills do current and future learners need to utilize in order to be successful?
What literacies practices and skills do instructors need to utilize in order to support the achievement of student success outcomes?
What literacies practices and skills do administrators need to utilize in order to support the achievement of student success outcomes?
Which of the following literacies are essential for effectively integrating AI into educational settings?
Briefly describe your role and how it relates to the development of AI literacies in educational contexts.
Describe the ways you are developing your own AI literacies in both professional and personal settings.
In what ways are you observing learners applying AI-related literacies into their learning processes?
In what ways are you observing instructors applying AI-related literacies into their learning processes?
In what ways are you observing administrators supporting the development of AI-related literacies across their organizations as a driver of institutional innovation and improvement?
Who should be responsible for teaching AI literacies within educational settings?
What practices should instructors and administrators engage in to ensure that the development of AI literacies is equitable amongst learners?
How do you see the roles of educators transforming with the increasing use of AI in education?
What specific literacies are needed to use AI tools in the development of Open Educational Practices (OEP)?
What technological or digital literacies are needed to use AI tools for open education?
What pedagogical skills are needed to use AI tools for open education?
How do AI literacies give meaning to open collaboration?
How do AI literacies support innovation in open education?
What are the ways in which communities can engage in developing and utilizing AI literacies for open education?
In your work, what practices have you seen employed using generative AI and open educational practices (OEP)?
What are the current considerations that educators need to be aware of with regard to generative AI and open pedagogies?
What are the potential difficulties that educators must mitigate in successfully integrating generative AI in the classroom in ways that protect and advance openness?
What foundational processes and systems do leaders need to build in order to harness the power of generative AI for open education, as well as mitigate its potential harm?
Appendix B: Interview Protocol
Briefly describe your role in education, and in particular, share:
Which open educational practices (OEP) do you engage in as part of your role?
Which AI literacies do you employ as part of your role?
What types of open educational resources (OER) have you seen people creating using AI?
Do you see OEP supporting the development of AI literacies? And if so, how?
Do you see AI literacies supporting the development of OEP, and if so, how?
What do people need to know about technology in order to use AI for open education work?
What content or subject knowledge do people need to have in order to use AI for open education?
What should people know about pedagogy and the learning process in order to use AI for open education work?
How might AI be used to improve the ways that we create OER?
How can AI help us improve the impact of open education in directly supporting the communities that we serve?
What effect do you think AI is having on the ways in which people collaborate on open education projects?
How do you see AI literacies and open pedagogy as connected?
Appendix C: Acknowledged Scholars
Susan Adams (US)
Aneesha Bakharia (Australia)
Maha Bali (Egypt)
Doug Belshaw (UK)
José Antonio Bowen (US)
Aras Bozkurt (Türkiye)
Melody Buckner (US)
Kiran Budhrani (US)
Liz Chase (Australia)
Melody Chin (Singapore)
Julie Curtis (US)
Van Davis (US)
Vincent Del Casino, Jr. (US)
Reed Dickson (US)
Gerry Hanley (US)
Reed Helper (US)
Isabel Hilinger (Chile)
Lisa Jacka (Australia)
Vistasp Karbhari (US)
Julie Lindsay (Australia)
Liza Long (US)
Kathryn MacCullum (New Zealand
Ebenezer Malcalm (Ghana)
Punya Mishra (US)
Julian Moore (Australia)
John Okewole (Nigeria)
Ebba Ossiannilsson (Sweden)
Nicola Pallitt (South Africa)
David Parsons (New Zealand)
Samantha Seah (Singapore)
Judith Sebesta (US)
George Siemens (Australia)
Melissa Vito (US)
Leigh Graves Wolf (Ireland)
Rosa Maria Vicari (Brazil)
Miguel Vieira (Brazil)
Wen Wen (US)
Appendix D: Dimensions of AI Literacies
By Drs. Angela Gunder, Joshua Herron, and Nicole Weber, with significant contributions from Drs. Colette Chelf and Sherry Birdwell. This taxonomy is a remix of Doug Belshaw’s book, The Essential Elements of Digital Literacies, and is licensed under CC BY-NC-SA 4.0.
The Dimensions of AI Literacies were designed to meet the increasing demand for skills that enable educators, learners, and leaders to understand the phenomena surrounding AI in education, particularly in developing uses aligned to socioculturally situated values. Adapted from Doug Belshaw's Essential Elements of Digital Literacies3, this framework recognizes AI literacies as a broad, interconnected spectrum of competencies, rather than a reductive divide between those who are AI-literate and and AI-illiterate. By viewing AI literacies as a diverse set of skills, this taxonomy offers nuanced insight into how AI can enhance teaching and learning across various cultural and social settings. This perspective supports educators in crafting inclusive and flexible learning experiences, empowers learners to approach AI tools with critical and creative thinking, and equips leaders to drive responsible and effective AI integration within their institutions. Moreover, as AI technologies proliferate and grow in sophistication, this taxonomy provides strategists and practitioners with a responsive vocabulary to navigate the swiftly shifting AI landscape in education. These dimensions give educators and leaders a foundation to foster collaborative, reflective discussions on AI, promoting the development of skills that will meaningfully influence the future of education.
CULTURAL Recognizing the connections between people, AI-informed resources and tools, and points of engagement within AI tools and AI-enabled environments.
CHARACTERISTICS
Cultural AI Literacies involve understanding the accepted contexts in which AI tools are used by learners, educators, and administrators, recognizing the sociocultural norms and practices that shape these contexts. They include learning how others employ AI tools within teaching and learning environments, encompassing the various inputs, such as prompt engineering, and the processes, like iterative conversations with large language models (LLMs) to enhance the quality and accuracy of generated content. Additionally, cultural AI literacies require familiarity with the different outputs produced by AI tools, such as multimodal artifacts like text, graphics, and videos. By understanding these diverse applications, individuals can use AI in ways that are culturally sensitive and aligned with educational goals.
EXAMPLE APPLICATIONS BY ROLE
A learner can use AI tools to explore cultural perspectives regarding current or historical events, comparing how AI databases provide information based on cultural inputs. Additionally, they can explore how AI-generated content such as text, images, and videos may be interpreted differently in diverse cultural and educational contexts.
An educator can apply cultural AI literacies by recognizing how different student populations interact with AI tools. This involves tailoring instructional content to fit the cultural backgrounds and learning preferences of diverse students. Educators can use AI tools to generate culturally responsive materials, such as adapting case studies, examples, or even feedback that reflect the values and norms of the students' communities.
A leader can analyze data from various student demographics to develop culturally informed strategies for AI tool implementation. They can use AI to facilitate cross-cultural communication within educational communities, ensuring that AI initiatives and policies are inclusive, equitable, and sensitive to the cultural contexts of the educators and students involved.
COGNITIVE Expanding intellectual capabilities by engaging with AI-enabled processes and environments.
CHARACTERISTICS
Cognitive AI Literacies involve developing the skills necessary to navigate various AI environments to build knowledge and understanding effectively. Just as physical strength is built through regular exercise, cognitive AI literacies require active engagement with different AI tools and systems, fostering exploration and play to develop familiarity and expertise. This process also entails exposure to a wide range of data sets and knowledge sources, recognizing that AI tends to generalize based on the most dominant narratives present. Furthermore, cognitive AI literacies involve understanding the strengths and limitations of AI—identifying what AI tools are well-suited to handle and recognizing the tasks that are better performed by humans. This balanced approach helps ensure that AI is used appropriately and effectively in educational contexts.
EXAMPLE APPLICATIONS BY ROLE
A learner can use AI-powered platforms to identify patterns in research data or generate hypotheses based on various scenarios. By navigating these AI environments, learners build intellectual agility, learning how to evaluate AI outputs critically and apply them to their own projects and studies.
An educator can use AI to create adaptive learning experiences that challenge students at different levels of understanding, fostering deep cognitive engagement. Additionally, by integrating AI into their teaching practices, educators help students recognize when and how to question AI outputs, ensuring they understand AI's capabilities and limitations in solving complex problems.
A leader can facilitate professional development opportunities that encourage educators to engage with AI tools, promoting a culture of continuous learning and cognitive growth within the institution. By fostering an environment where AI is used thoughtfully, leaders ensure that AI’s capabilities are maximized to enhance both teaching and administrative practices without diminishing human judgment.
CONSTRUCTIVE Utilizing AI tools to build, remix, and generate new content, applying AI capabilities.
CHARACTERISTICS
Constructive AI Literacies involve understanding what it means to construct, build, or make something within AI-enabled environments. These literacies encompass practices of remixing, where AI tools are used to help individuals create new content by adapting and revising existing materials. Given the complexities of attribution in many AI-generated works, developing constructive AI literacies also aligns closely with critical AI literacies. It requires users to identify the original sources of content, ensure proper credit is given, and navigate the ethical implications of appropriating and remixing material. This approach fosters a responsible and creative use of AI, encouraging innovation while respecting intellectual property and ethical considerations.
EXAMPLE APPLICATIONS BY ROLE
A learner might use AI to assist in remixing information from different sources into coherent arguments or generating creative visual designs for presentations. By interacting with AI in this way, learners not only enhance their technical skills but also improve their capacity for critical thinking and synthesis, turning AI outputs into original, well-crafted work.
An educator can utilize AI to remix content from various sources, such as generating quizzes tailored to the needs of specific students or adapting lesson plans to different learning styles. Additionally, educators can encourage students to engage in constructive projects, where they apply AI tools to generate their own learning materials or collaborate on AI-driven group projects.
A leader can leverage AI to generate innovative content that supports decision-making. Leaders can also create AI-enhanced professional development programs for staff, using AI-generated insights to tailor content and learning experiences to meet the diverse needs of educators and administrators.
COMMUNICATIVE
Leveraging AI technologies to convey ideas effectively, recognizing the sociocultural practices and nuances that AI interprets and influences in different settings.
CHARACTERISTICS
Communicative AI Literacies focus on using AI tools to convey meaning for a specific purpose, ensuring that ideas and messages are effectively communicated. These literacies are closely linked to Constructive AI Literacies, as they involve creating or crafting content to express an idea or message. They also connect with Cultural AI Literacies by requiring an understanding of discourse style, tone, and voice to tailor communication appropriately for different audiences and contexts. Furthermore, Communicative AI Literacies recognize the social dynamics of AI use, where both human and technological actors interact—understanding how one communicates with AI tools, such as chatbots, directly influences the outputs received. These literacies promote effective communication in an AI-enabled world by blending technical skill with cultural awareness and purposeful intent.
EXAMPLE APPLICATIONS BY ROLE
A learner can utilize AI tools to enhance their communication by refining ideas, adjusting tone, and tailoring their messages for different audiences, including cross-culturally.
An educator can use AI tools to improve the clarity and tone of content and feedback for their learners. In this way, AI can streamline feedback, offering personalized suggestions to students in a clear, constructive manner, enhancing the learning experience and improving overall communication with learners.
A leader can leverage AI to assist in generating well-structured, data-driven responses to feedback. By streamlining communication processes and improving responsiveness, AI supports leaders in managing feedback and maintaining open, productive dialogue.
CONFIDENT Developing the ability to solve problems and manage learning within AI-driven environments by understanding and harnessing their unique features and potentials.
CHARACTERISTICS
Confident AI Literacies emphasize developing self-assurance and self-reliance in using AI tools to generate ideas, put those ideas into action, and evaluate their effectiveness and impact. These literacies cultivate a sense of curiosity and a willingness to explore new AI tools and AI-enabled environments, understanding that there is freedom to fail and that failure serves as a valuable data point for iterative improvement. Confident AI literacies also involve being open and authentic about both successes and challenges, fostering a growth mindset that embraces learning from mistakes. They include the ability to manage one’s own learning and solve problems by leveraging feedback generated by both human instructors and AI tools. Developing these literacies requires normalizing assessment processes and applying feedback within a networked community of practice, where continuous learning and adaptation are encouraged and supported.
EXAMPLE APPLICATIONS BY ROLE
A learner can use AI platforms to generate study plans, ask targeted questions, or seek explanations for difficult concepts. By experimenting with different AI features—such as adaptive learning environments or AI-generated feedback—students build confidence in their ability to manage their learning, making them more autonomous and proactive in pursuing their educational goals.
An educator can use AI tools to enhance their teaching practices and troubleshoot classroom challenges. Educators who are confident in AI’s capabilities can deploy it effectively to address individual student needs, provide personalized feedback or develop alternative instructional strategies.
A leader can harness AI-powered data analytics to inform policy decisions, track institutional progress, or predict future trends. This confident approach empowers leaders to use AI not just reactively but proactively, solving problems and guiding their organizations through the evolving AI-driven landscape.
CREATIVE Engaging in ideation and generative actions using AI, focusing on how AI can add value and introduce new possibilities within specific contexts.
CHARACTERISTICS
Creative AI Literacies challenge the misconception that creativity is an innate trait, emphasizing instead that it is a skill that can be developed and nurtured. These literacies involve the ability to generate new ideas, put them into action, and assess their value and impact, with these evaluations shaped by both individual insights and community standards. Creative AI literacies encourage experimenting with new methods and processes to achieve outcomes that were previously difficult or even impossible, requiring a strong sense of innovation and the ability to make sense of novel situations. They also rely on the confident literacies of being willing to take risks and exercising a level of autonomy and agency in exploring AI's possibilities. Furthermore, creative AI literacies are inclusive of reimagining learning activities for an AI-enabled world, fostering an environment where both educators and learners use AI tools to enhance creativity and drive new approaches to teaching and learning.
EXAMPLE APPLICATIONS BY ROLE
A learner can brainstorm innovative solutions, generate unique content such as essays, art, or multimedia projects, and remix existing materials to fit new contexts. For example, a student might use an AI text generator to draft an outline for a research paper or utilize AI image tools to create visual representations for a class project.
An educator can use AI to co-create dynamic lesson plans, simulations, or assessments that allow for creative exploration.
A leader can use AI tools to envision novel strategic initiatives, generate new programs or policies, or even create more engaging and interactive professional development experiences for staff.
CRITICAL
Examining the power dynamics and ethical considerations inherent in AI practices, reflecting on the broader societal impacts of AI-driven decisions and actions.
CHARACTERISTICS
Critical AI Literacies are among the most essential literacies in this early stage of AI integration in education. They begin with examining the power structures embedded within AI environments, identifying who or what is privileged in these systems, and understanding who is included or excluded from data sets or environments. This involves recognizing the biases present in AI tools and questioning the assumptions behind these decisions. They require users to spot inaccurate or misleading outputs generated by AI and assess the accuracy and veracity of content. Additionally, they involve understanding how and where user inputs are used, including awareness of when content inputs may be training other large language models (LLMs), and addressing related privacy and security concerns, such as compliance with institutional policies and federal regulations. Moreover, these literacies include using AI-generated data to make informed, critical decisions about strategy, resource allocation, and operational processes, ensuring that AI tools are applied responsibly and ethically in educational contexts.
EXAMPLE APPLICATIONS BY ROLE
A learner can critically assess the data sources an AI tool uses to generate responses, ensuring they understand how these data might reflect dominant cultural or ideological perspectives.
An educator can explore how an AI grading system could inadvertently favor certain student demographics and take steps to mitigate such biases. Educators also reflect on their ethical responsibilities when integrating AI into their classrooms, ensuring that AI enhances rather than diminishes critical thinking.
A leader can examine the use of AI in student admissions or performance assessments, ensuring that the technology does not inadvertently reinforce systemic inequities.
CIVIC
Employing AI knowledge and skills to contribute positively to society, using AI to foster community empowerment, engagement, and societal progress.
CHARACTERISTICS
Civic AI Literacies focus on using AI knowledge and skills to contribute positively to society by fostering more equitable and inclusive learning environments. These literacies involve considering how AI can help create culturally affirming content that promotes a sense of belonging and community in the classroom. Civic AI literacies also emphasize the use of AI to achieve adaptive and personalized learning, which respects and builds upon learners' past experiences and skills while meeting them where they are to further their knowledge. Additionally, these literacies encourage creating openness around AI usage, addressing issues like the digital divide and ensuring equitable access to AI tools and their development. Ultimately, Civic AI Literacies view AI as an enabler for broader participation in society, empowering individuals to engage more fully in civic life and contribute to societal progress.
EXAMPLE APPLICATIONS BY ROLE
A learner can leverage AI tools to analyze social issues, create content that raises awareness, or develop solutions to community problems.
An educator can guide learners in applying AI tools to analyze social justice issues, develop community projects, or contribute to civic discourse.
A leader can use AI to enhance transparency in institutional decision-making or to identify and address community needs. For instance, an educational leader could use AI to gather feedback from students, educators, and community members, ensuring that policies reflect diverse perspectives and promote civic responsibility.
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