Algorithmic Literacy

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A Pathway Forward: Valuing the “Why” and “How” of Learning 

In our current information environment, artificially intelligent, algorithm-driven technologies mediate essentially all information and communication, and they demonstrably influence our decision-making and the ways we participate in society. Yet despite their ubiquitous integration into our lives, most of us have little clarity on how these algorithms function, the human intentions and socioeconomic structures behind them, and how they contribute to the broader societal impacts we see playing out on the news, in social media, and in our day-to-day interactions. Information now acts upon us as platform algorithms seek us out, rather than the other way around (Bull, MacMillan & Head 2021). Today’s increasingly information-centric jobs require comfort with open-ended questions for which there are not always clear “right” answers, as well as working with information sources whose authority is unclear or whose accuracy may be hotly contested (Fister 2022).  

In academia, teaching students to use information has traditionally looked like collecting relevant scholarly sources to support one’s existing views in an essay or research paper, rather than a process by which to learn broadly or deeply about a topic and develop new ideas. Largely, this learning experience occurs in the controlled, curated environment of library databases, course reading lists, and peer-reviewed journals. However, generative AI tools and algorithm-driven social media platforms have demonstrated that this approach to information seeking does not prepare us for the more complex “real world” information interactions we experience outside the classroom, nor does it build upon valuable skills developed in those “non-academic” interactions that could be assets in scholarly pursuits. However, when academic work is co-designed by faculty and students around students’ natural curiosity and their desire to contribute to the common good, algorithmic tools can be understood not as a shortcut to “get the grade,” but as a helpful support for learning and creation. 

Developing algorithmic literacy will play an important role In our response to the changes AI brings to our information and education environment. In the following sections, you will find a rationale for employing algorithmic literacy practices – an approach to teaching and learning that empowers creative, critical, and participatory uses of these technologies. Then, you’ll find four guiding approaches to apply algorithmic literacy practices in your teaching and your students’ learning experiences. Finally, you can utilize the example activities and reflection questions to ideate algorithmic literacy strategies specific to your teaching context and your students’ needs. 

The classroom is the ideal place for exploration and messiness in student-driven inquiry and knowledge creation, especially because of the unique opportunities a class dynamic provides for midcourse-correction with feedback and collaboration. This sort of open-ended, self-determined classroom information use allows students to be supported while they learn in ways that look much more like the complex information landscape they encounter outside the classroom. As artificial intelligence assumes increasing decision-making power in our society, it is critical that we teach and learn in ways that help us recognize important decisions, make informed choices, and differentiate between truth-seeking and truth-claiming (Fister, 2021). Algorithmic literacy practices provide a pathway forward to operationalize this approach, blending socio-technical algorithm awareness, critical information literacy, and high-impact teaching and learning practices. This approach to teaching and learning with AI emphasizes information use that moves us all toward transformative action and greater societal participation.  

Informed Teaching and Learning with AI: Applying Algorithmic Literacy 

Algorithmic literacy can be seen as an evolutionary step in our approach to information literacy and learning. It shifts attention to understanding and evaluating the algorithmic systems themselves that now determine which information we see, as well as how those systems impact human actions. Algorithmic literacy practices guide us to be attentive to the implications of how information gets to us, how it fits within a wider network of perspectives and sources, and how we respond to it. Ultimately, it points us to research and information gathering for the purpose of constructing a clearer picture of a topic – learning for the sake of learning – rather than piecing together disparate pieces of knowledge to justify a point or say we “cited the experts.”  

This shift to “zoom out” and see the bigger picture enables us to choose how to engage by considering the ways algorithmic systems are designed to manipulate our interactions with the information they present, or acknowledging the ways in which they might fail to represent certain perspectives. Rather than picking and choosing information sources based solely on the quality of their content (which can be challenging for non-subject-experts) or signals of source trustworthiness (which can be easily falsified to leverage confirmation bias), algorithmic literacy takes a broader, more holistic approach to the ways we use information to learn. Here are four suggested teaching and learning approaches to collaboratively develop algorithmic literacy practices in higher education classrooms: 

Basic knowledge of the technical and socioeconomic processes behind algorithmic platforms provides clarity on how and why we see the information we do. And, equally important is examining the personal side of these processes – how our own cognitive, affective, and behavioral processes are impacted by algorithmically-mediated information. We suggest four algorithmic awareness competencies (Augustinus, 2022; Long & Magerko, 2020) as particularly important to develop ethical, effective processes for learning with AI:  

  1. Identification of algorithms in daily information interactions 
  1. Explanation of how algorithmic platforms function 
  1. Evaluation of the strengths and limitations of algorithms 
  1. Integration of ethical and social impacts in our use and regulation of algorithms  

These foundational skills and knowledge about the nature and functionality of algorithmic technologies can be approached broadly, in the context of personal information use, or in the context of how one interacts with information in a particular discipline or field. By examining 1) the processes by which information gets to us, 2) our own learning processes in relation to that information, and 3) the processes by which people participate in knowledge creation, the focus of learning shifts to developing a holistic understanding of the content, rather than merely providing the “right” answer to get the desired grade. A process orientation positions students to both demonstrate their learning and apply it across contexts. When learning how to learn is paramount, using AI tools to do the work for you can be less appealing. As students document the steps they take and the tools they use, receive feedback, work collaboratively, and improve, the process of learning becomes just as valuable as the product or answer itself. Confidence in the process is what equips students to be successful across contexts – academic, professional, or personal. 

Example activities to consider 

  • Together as a class, explore or test out an AI tool’s capabilities and report out on findings or challenges encountered. At each step in the process, ask students to describe what they are doing and seeing, and why they think it might be happening. Discuss potential benefits or concerns of using the tool. 
  • Explore differing outcomes of completing the same task if each student uses the same prompt with an AI tool or, alternatively, if each student writes a unique prompt to use the same AI tool. Explore why outcomes might be different for different users or different prompts. 
  • Shared responsibility: As you consider AI use in your course, expand beyond the prescriptive “use AI for this; don’t use it for that” to a conversation and a question: “Why does it matter where AI comes into our work? Where is human decision making most important?”  

Reflect and apply: Process orientation 

  • Where am I placing emphasis on demonstrating and evaluating the process of finding information or solving a problem?  
  • Where am I placing emphasis on providing an answer or product?  
  • How might I provide more opportunities for feedback, revision, and collaboration? 
  • In my teaching context, at what point(s) is it most critical for students to differentiate and make informed decisions about the pros/cons of using AI? How might I guide them to explore the benefits of taking time to work through a process that might be easier or faster using AI? 

Zoe Mayhook, Assistant Professor, Libraries & School of Information Studies

Prof. Zoe Mayhook (Assistant Professor, Libraries & School of Information Studies) partnered with a student to develop a process-oriented workshop about empowering the critical evaluation and use of AI tools for research: “Working with a student partner made me appreciate that students are also thinking critically about the benefits and shortcomings of AI tools, including the limitations of using these tools as information sources. My student partner and I assessed various AI tools tailored to support academic research, which subsequently informed a workshop session on utilizing AI tools for literature reviews. Aligning with the algorithmic literacy process orientation allowed us to cultivate algorithmic awareness through the exploration of these AI tools, and then we engaged in shared decision-making in considering how students might best learn and benefit from this information.” Mayhook explained that the algorithmic awareness competencies ”equipped me with valuable approaches to engaging students in the evaluation and ethical use of AI tools, both in academic and professional contexts.”

Developing the ability to engage in effective self-guided, open-ended inquiry requires intention and practice. This is especially important as we interact with algorithmic platforms that deliver information stripped of context necessary for evaluating how and when to integrate that information. For example, the list of links returned in a Google search are all presented in one place with a uniform appearance, with no indication as to the reasons behind the order of results. In an example search – “mutated flowers fukushima” – varying sources seem to indicate very different answers about whether wildflowers are being mutated by lingering radiation in Fukushima, Japan. Algorithmic literacy cultivates a mindset and toolkit of inquiry, by which use the many information tools at our disposal to seek broader context as a necessary component for decision-making (Caulfield, 2017). Instructors can invite students to learn the power of asking and investigating questions that are meaningful to them. As students make decisions and bring more of themselves to their work, the learning process can be increasingly self-motivated. In concert with many sources – academic journals, social media, community members or subject-matter experts – AI tools can be explored as methods for efficient, productivity-boosting inquiry, rather than answer-generating shortcuts. Rather than providing students with all the necessary information or pre-determining the sources they will need, we can provide them with opportunities to pose and investigate questions of their own about which information they need and where they might look for it, as well as thinking divergently to seek out multiple perspectives for a broader view of the topic (What Is the QFT?, 2023). This has the added benefit of introducing students to the cyclical nature of research and learning, and it demonstrates that creating and sharing information is a networked and social process (Fister, 2022).  

Example activities to consider 

  • Peer teaching: When starting a course or a new unit, have students first search independently for information about how AI could be used for learning generally, for a particular topic or task, or for professional work in a particular field, and then share what they find in class. 
  • Peer evaluation: when using AI, structure opportunities for formal or informal peer evaluation – this could look like engaging in a think-pair-share type activity, where students have the option to use AI for a task, then share the results and any observations or challenges with a classmate. Then, each can provide a check on one another’s decision-making and outcome by comparing their experience, asking critical questions, and ideating next steps. 
  • Group problem solving: provide students with a problem or prompt to solve with minimal initial instruction, but require groups to document and share their process of exploration – the tools they use (AI included), how and why they use them, where they run into challenges, and what they might try next to overcome the challenges. 

Prof. Jung Joo Sohn (Assistant Professor, Art & Design) found the algorithmic literacy practice of shared inquiry very powerful in thinking about AI in his design-centered courses. He shared how open conversations with students about AI have helped him better understand some motives behind AI use that he might not agree with: “Working with a student partner significantly enhanced my approach to discussing with students and using AI in my classes. Students may use AI because they think it’s easier…when assignments are unclear or too hard, students can lose motivation.” This dialogue has given him some ideas for collaborative inquiry into where to draw the line for using AI in creative work: ”This shows why it’s important to teach algorithmic literacy: not just technical skills, but also critical thinking. I think it would be good to make models that explain how these AI-use decisions work.”

Reflective practice benefits learning, professional work, and personal growth in many ways. It deepens and documents learning, provides clarity on the purpose for our actions, and informs our next steps (Ash & Clayton, 2009). This is particularly critical in algorithm-mediated information interactions, where the tech tries to remove as many steps of the information gathering process as possible. Algorithmic platforms tend to prioritize information that will generate an immediate, emotion-driven reaction in the audience. For example, social media feeds elevate posts that have garnered the most interactions (likes, retweets, etc). We can integrate reflective exercises into assignments or discussions that help form a habit of pausing to examine how our own preconceptions and emotions impact our perception of the accuracy or trustworthiness of information (Oeldorf-Hirsch & Neubaum, 2021). Reflection can look many ways: exit “tickets” to capture learning at the end of class; guided reading questions during a homework assignment; identifying one’s own previous experiences that inform the way one learns; peer- and self-evaluation; and more. Reflection serves multiple purposes, both enabling personal growth, creating a community of learners, and serving as formative assessment. This reflective approach to information enables us to retain our decision-making power amidst algorithmic systems that are designed to make decisions for us. 

Example activities to consider: 

  • Provide structured guidance and expectations for students to engage in self evaluation. This could look like asking students to set goals for the semester and then inviting them to be part of evaluating how well they achieved those goals. When students’ goals are clear and personally meaningful, it may help when making choices about when or when not to use AI for coursework. 
  • As is feasible, regular student check-ins with an instructor or TA will provide opportunities to identify areas of both strength and struggle, as well as build relationships. The benefits of these reflective meetings demonstrate that learning and information are socially networked, and that AI tools cannot replace the unique affordances of human-to-human interaction, but rather can supplement them. 
  • When students engage in reflection, help them to move beyond simply reporting events or commenting with their opinion – provide guiding prompts that aid in identifying challenges and next steps in overcoming those challenges or building on their experience. When learning with AI, it is particularly important to develop the reflective practice of identifying “now what” – how will I apply and transform the information I’ve found for my particular context? AI tools make it very easy for our brains to “check out” but it is essential that we train our brains to view these tools not as decision-makers, but option-presenters. 

Reflect and apply: Reflection 

  • When might I add moments for students to pause and notice, document, or share their own thoughts, actions, reactions?  
  • How do my own emotions impact the information I decide to share with my students?  
  • How can I model my own process of reflection and how it impacts my research and teaching? 
  • In my teaching context, at what point(s) is it most critical for students to examine their motivations, assumptions, and previous experiences and how those influence their reasons for doing things? How might I help students identify a plan of action for using AI critically, taking these insights into consideration? 

Annaelle Gackiere (Psychological Sciences and Anthropological Sciences) says, “Reflecting on courses I have taken in the past, I remember noticing strong reactions against AI use in the classroom, to the point that I was not made aware of its complexities, the ethics tied to it, and the possibilities of its use in writing-heavy contexts.” She explains that partnering with a professor who was open to discussing the possibilities of using AI helped her enact the algorithmic literacy practice of reflection: ”This partnership changed my outlook quite drastically. Now, I look over the syllabus more critically and think about the reasoning behind the extent to which we can use AI. I also assess my behaviors and choices, thinking more deeply about what I would want to change about decisions made in my classes.”

The overarching goal of algorithmic literacy is to cultivate education for democracy – to prepare students to meet the world’s complex questions and varied perspectives with curiosity, open-mindedness, and a belief that their actions make a difference. Algorithmic literacy approaches create opportunities for ‘transformative action’ – collaborative, participatory learning experiences with real-world connections, which lead to taking action that challenges or improves the status quo. Students, like all of us, want to contribute their strengths and be challenged to do work that matters to others. Transformative action builds on the approaches mentioned earlier to create a learning environment where students view their course work as a vehicle to actively improve the world around them with their own ideas. Developing a process orientation in your classrooms develops the skills to put information in context, which is key to identifying how that information can be used reliably. Developing inquiry-based learning in your classroom develops a question-posing mindset toward learning with algorithmically-mediated information. These approaches bridge in-class and out-of-class information practices by inviting students to apply prior knowledge they bring from unique experiences to their work in class. Students use algorithmic social media platforms all day, every day, and they have already developed skills and strategies for evaluating information and communicating ideas tailored to a particular audience (Head, Fister & MacMillan 2020). Some approaches to integrating transformative action could include: ask students to create examples or documentation that will support future students in the use of AI for learning; replace a final exam with an experiential or service learning project to create an AI tool that benefits a community; engage in a negotiated curriculum, where your students help determine when and why AI can be used, and can even sign up to teach a lesson or lead discussion around the use of AI. There are as many ways to make learning transformative as there are students – asking them what matters most to them is a great place to start. When our teaching and learning culminates in taking pro-social action to improve the world around us, it requires both instructors and students to draw on unique personal experiences and strategies for “real world” information evaluation. It creates opportunities for collaboration and learning from one another as we encounter new types of information and practice new skills.  

Example activities to consider 

  • Rather than making a unilateral or inflexible decision about when and why to use AI in your course, collaboratively develop your course AI policy with your students. This can create an opportunity for students to take ownership of their own learning outcomes as they consider how AI might positively or negatively affect their ability to learn what they want to learn.  
  • Consider ways that you might feature student work as examples, documentation, or study materials for future students. For example, if students document the prompts they used with an AI tool and discuss what was successful or unsuccessful, those experiences could be used as guidance for other students learning to use AI productively. 
  • When developing assignments or projects, challenge students to take on applied, real-world problems or scenarios. Projects with meaningful outcomes for a broader community or for the students’ future work can be a huge motivator – importantly, this could include working with your students to identify what exactly they might find to be a meaningful outcome, how they envision AI as a support mechanism achieving it, or if AI might be a hindrance in some cases.  

Reflect and apply: Transformative action  

  • What is the most essential purpose or goal of the work I am asking students to do?  
  • Is the purpose made explicit, especially if it could be perceived as “busy work” or some other undesirable task?  
  • How might I provide opportunities for them to envision how their knowledge and experience can uniquely contribute to the work they are doing? 
  • In my teaching context, at what point(s) is it most critical for students to influence the conversation around how to approach AI for learning? How might I involve students in designing ways of working with AI with outcomes that they find meaningful? 

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