Information, Digital, Media, and Algorithmic Literacies

Literacy is more than just reading or writing. It is about understanding, evaluating, and practicing learned knowledge. There are several types of literacies, and many are intertwined, such as information literacy, digital literacy, media literacy, and AI or algorithmic literacy. General definitions and differences among these literacies include:

Information literacy is the need for careful retrieval and selection of available information, which involves critical thinking, meta-cognitive, and procedural knowledge used to locate information (Koltay, 2011). It is a process of recognizing the need for information and then identifying, finding, evaluating, organizing, and using the information (Bawden, 2008).

Digital literacy is the ability to understand and use information from various digital sources (Gilster, 1997). This includes an awareness, attitude, and ability for someone to “appropriately use digital tools and facilities to identify, access, manage, integrate, evaluate, analyze and synthesize digital resources, construct new knowledge, create media expressions, and communicate with others” (Martin, 2005, p. 135). Today, this encompasses digital devices and actions such as surfing the web and effectively using applications, social media, and databases.

Media literacy is the ability to “encode and decode the symbols transmitted via media and synthesize, analyze and produce mediated messages,” including text, images, and sound. This also includes assessing the influence of those media messages on society and using and creating media responsibly.

Algorithmic literacy—the ability to “recognize, grasp, use, and critically assess artificial intelligence technologies and their impacts.” Although several AI literacy frameworks are discussed in the literature, they address literacy through major domains, such as ethical, functional, pedagogical, and rhetorical. According to the Teaching Commons at Stanford University, these intersectional AI literacy domains help us to address key questions:

• How do we navigate the ethical issues of AI? (ethical literacy)
• How does AI work? (functional literacy)
• How do we use AI to enhance teaching and learning? (pedagogical literacy)
• How do we use natural and AI-generated language to achieve our goals? (rhetorical literacy)

Each of these literacies is essential to education, and the educational strategies are used to ensure every student becomes an informed citizen. Therefore, these literacies are especially important when designing, selecting, and evaluating what educational technologies to use for an educational framework.

Principles of Human-Centered Design

Just as there are many types of literacies, there are many ways to design a product that we use to access information, evaluate media, apply digital skills, and assess AI, among other activities. The evolving nature of education, technology, and literacy requires that such designs focus on the user experience of a product. According to design expert Don Norman, you learn about people to help find the most appropriate solution for those using that product. Hence, his term, human-centered design, is a practice used by designers to focus on people’s needs by empathizing with them and their needs when designing that product. Human-centered design can be applied to any product, including educational technologies, using its four key principles:

• People-centered: Using participatory design to ensure a focus on people and their context
• Understanding and solving the root / right problem
• Thinking of everything as a system of interconnected parts
• Iterative work to try small and simple interventions to learn from and improve upon

Participatory design is a human-centered design process involving stakeholders and designers working together to ensure the population’s needs are met (Henry et al., 2007). Participatory design and its narrower focus of co-design have a primary goal to co-create solutions to the root problem using the expertise and knowledge of a person with an authentic experience (e.g., active educational technology user or lived experience of a disability) for the issue being investigated (e.g., user experience of a learning management system or online research study participation).

Human-Centered AI and Inclusive Design

Inclusive design builds off the human-centered design in that it addresses the people-centered aspect. People and their experiences are diverse, especially when there are a number of combinations of different identity characteristics. For example, people with disabilities make up 1.3 billion people worldwide. Given these statistics, designing products with the viewpoint of someone with visual, hearing, motor, or cognitive challenges (whether permanent, temporary, or situational) can impact many people (Miller, 2025ab). Consider how responsive screen sizes with zoom capabilities and captions on media help not only people who are blind or Deaf but also people with low vision, who are hard of hearing, or in a noisy environment. Thus, designing with an inclusive and accessible mindset can benefit a lot of people.

Similarly, inclusively designed online surveys and interview and user testing protocols can also impact research study experiences in higher education by including people with a range of human abilities (Miller, 2025ab). This is accomplished by applying human-centered design, which focuses on problem-solving by building empathy for human behaviors and human needs. Participatory design is often used alongside universal design and human-centered design research, where stakeholders and researchers collaborate on the research and design process, ensuring the needs of the intended user group are met (Henry et al., 2007; Miller, 2025ab). Ultimately, studies have found that when applied early and often, inclusive design practices may increase access to research study participation for marginalized groups, thus providing a more accurate representation of the user population and enhancing data quality and validity (Miller, 2024, 2025ab). From these results, we can anticipate similar outcomes when applying human-centered AI design to educational technology, thereby increasing the potential for more equitable and accessible educational experiences.

After considering these inclusive and human-centered design principles and recalling those four domains of AI literacy introduced earlier, we can see a fifth domain (or key question) missing from the AI for education framework: human-centered AI, or why do we engage with AI? How can everyone engage AI? This requires us to apply, design, and think with accessibility and usability as a critical domain.

Human-centered AI domains of accessibility and usability examples include but are not limited to:

• Text-to-speech which reads text aloud and is especially helpful to people with visual impairments, reading difficulties, and people with auditory learning preferences
• Speech recognition or transcription tools that transcribe spoken language to a written form which is important for people with hearing impairments, people who may have difficulty with reading and writing, or students preparing study materials
• Closed captioning tools that automatically generate captions for video and multimedia content, which is helpful for people with hearing impairments, second language learners, and people watching the content in noisy environments
• Language translation tools that translate speech and text into another language provide learners access to information, helping users learn and retain the content

These examples demonstrate how critical such applications are for everyone to access, use, and participate in current literacy frameworks and mediated communications. Inaccessible AI tools can prevent anyone, including people with disabilities, from social and educational participation. That reinforces existing barriers and creates new ones as AI technologies continue to evolve, especially when the AI technologies are created without co-designing those tools with diverse users.

Algorithms need to involve a human-centered design process that reflects the diversity of the human experience. The latest research shows that people with disabilities are now 16% of the world’s population, over 28% of the US and 27% of the European population. Disabled people represent the largest minority group, and these numbers increasingly show a move from a minority to a majority population (Miller, 2025b). These margins, or edge cases of the human ability spectrum, are increasingly affecting people broadly. Thus, human-centered AI design should be planned with this audience in mind.

Ways to Ensure Human-Centered AI EdTech

A few key guidelines to ensure creation and access to human-centered AI educational technologies include:

1. Use a co-design process early and throughout the design phases to create input from diverse learners, which include people with emerging disabilities—people with disabilities, impairments, or chronic conditions that are permanent, temporary, or situational (Miller, 2025b)
2. Understand the root problem and the interconnectedness of various literacy challenges (information, digital, media, AI) in the overall design and use of educational technologies.
3. Iteratively design and test with accessibility and usability in incremental stages to improve upon earlier designs.
4. The design and user testing teams should include subject experts, educators, students, people with various literacy skills and device experiences, neurodivergent people, and people who identify with a disability, impairment, or chronic condition.

When applied, these methods can help remove barriers and ensure that all learners have a more accessible and effective use of AI and educational technologies. By designing for the margins (edge cases or people with disabilities), AI systems and EdTech have a better chance of creating more equitable, usable, and enjoyable experiences for everyone.  Just as universal design principles should be integrated into AI systems, so too should inclusive design.

References

Bawden, D. (2008). Origins and Concepts of Digital Literacy. In C. Lankshear, & M. Knobel (Eds.), Digital Literacies: Concepts, Policies and Practices (pp. 17-32). Peter Lang Publishing.

Henry, A. D., Gallagher, P., Stringfellow, L. O., Hooven, F., & Himmelstein, J. (2007). Notes from the field: Contemporary strategies for developing surveys of people with disabilities: The mass health employment and disability survey. In T. Kroll, D. Keer, P. Placedk, J. Cyril, & G. Hendershot (Eds.), Towards best practices for surveying people with disabilities (pp. 127-146). Nova Science Publishers.

Koltay, T. (2011). The media and the literacies: media literacy, information literacy, and digital literacy. Media, Culture, and Society, 33(2), p. 211-221.

Martin, A. (2005). DigEuLit – a European framework for digital literacy: a progress report. Journal of eLiteracy, 2, 130-136.

Miller, A. (2024). Disabilities and user experience: An exploratory case study of survey and website accessibility. Journal of Accessibility and Design for All, 14(2). https://doi.org/10.17411/jaccess.v14i2.513

Miller, A. (2025a). Accessibility, disability, and inclusive instrument design: A critical literature review interrogating the user experience of online surveys and interviews. The International Journal of Information, Diversity, & Inclusion, 9(1/2).

Miller, A. (2025b). Human-centered design, disability, and accessible research experiences: A multiple method study, co-design framework, and model for inclusive instrument design [Doctoral dissertation, University of Missouri]. MoSpace Repository.

Norman, D. A. (2013). The design of everyday things. MIT Press.