3 Empowering Educators in the Age of Generative AI: A Critical Media Literacy Approach

The purpose of this chapter is to reflect our experience of teaching in the context of GenAI and share the instructional innovations we are implementing in response to this new context. We are both instructors of the same college course: a large, lower-division, general education undergraduate course that fulfills a university-wide diversity requirement, called Education and Film (EdFilm). Although we teach separate versions of the course, we collaborate on course design, lesson planning, and curriculum updates. In this chapter, we will describe instructional innovations we are implementing in the EdFilm course after careful thought, reflection, and dialogue. These include intentional tone-setting about GenAI and ongoing in-class discussions, as well as updated assessments and grading policies. In framing the issue of GenAI with students, we will apply the lens we already use to analyze mass media in our course, drawn from Critical Media Literacy (CML). As we will argue in this chapter, CML offers a helpful model for teaching with and about GenAI with students.

To set the stage for our discussion, we first review some framing literature about GenAI and describe the course we teach, including its learning objectives which are grounded in CML. With this context in mind, we then describe our instructional innovations for the EdFilm course, and close with implications for practice we hope will be applicable for college instructors across a variety of contexts and subject areas.

Framing Literature: The Effects of GenAI on Teaching and Learning

The public availability of GenAI tools is so new that peer-reviewed literature is lacking. However, preliminary studies suggest that GenAI is widely used by college students. In March 2023, the first academic semester that GenAI was publicly available, a survey of 1000 college students found that 43% had utilized a GenAI for class assignments (Welding, 2023). In another study conducted at a large public university in April of 2023, 40% of students surveyed said they had already used ChatGPT for academic coursework, and an additional 28% said they had considered using it (Scholarship of Teaching and Learning Artificial Intelligence Team [SoTL AI Team], 2023). Among those who used ChatGPT, 40% used it to complete assignments while 48% used it as a support tool to enhance content knowledge or engage in conversations based on their questions. Students who did not use ChatGPT cited reasons such as unfamiliarity with the tool, concerns about plagiarism, a lack of perceived need, and desire to learn independently. Those who viewed it as a form of plagiarism believed that using ChatGPT would inhibit their learning. This distinction reveals two primary groups of students: one that believes ChatGPT can be appropriately harnessed to enhance learning and another that considers it detrimental to learning.

The response of educational institutions to GenAI has been gradual and inconsistent. According to a study by Best Colleges in Spring 2023, more than half of college students (54%) reported that their instructors had not openly addressed the incorporation of AI tools such as ChatGPT (Welding, 2023), while 31% indicated that their instructors, course materials, or school honor codes explicitly prohibited the use of AI tools. This absence of guidance and communication highlights a significant gap in support for students in navigating the realm of AI technology. While some instructors carried on with their teaching as if nothing had changed, others were quick to prohibit and punish the use of AI tools. Meanwhile, in some corners of higher education, AI tools were uncritically celebrated for their potential to improve education by providing services such as one-to-one tutoring, teaching assistance, and course planning support for faculty (e.g. Lavadeier, et al., 2023; Han, 2023). What is missing from these options is a response that helps students learn to navigate not just the technology but the ethical and intellectual questions it raises.

By now, it is well-known to anyone who has casually followed the news on this topic that GenAI can produce a passing college-level paper with no human help. A student can simply provide a prompt and instructions (word limit, etc.), and receive a fully crafted essay without doing any of the required reading or independent thinking expected of them. But AI can also provide support for tasks like fact-checking, summarizing, proofreading, and grammatical editing (Terry, 2023; SoTL AI Team (2023). These uses are more akin to using built-in spell check or grammar tools in a word processing app. Where do we draw the line as instructors between positive and problematic uses of technology? Don’t we aspire to equip our students to use technology tools as part of becoming educated? We propose that instead of ignoring, demonizing, or uncritically celebrating this technology, we as educators should engage our students in critical questioning and exploration of its dangers, possibilities, and social impacts. In developing this approach, we were guided by the course we teach, EdFilm, which is framed by Critical Media Literacy (CML). Below, we describe the EdFilm class and explain the CML framework, to show how we will apply it to GenAI.

Learning Context: EdFilm Class

EdFilm is a lower-division undergraduate course offered at a large public university in the northeastern part of the US. It attracts students from all majors and departments, fulfilling students’ diversity course requirement. The class is offered two ways: One section serves around 100 students, led by a professor (Author 2) with graduate TAs; another section is a specialized course for first-year students (n=30) and is taught by a single instructor (Author 1). Author 2 designed the course and has taught it fourteen times over ten years. Author 1 served as a doctoral Teaching Assistant for the large course for 6 semesters, and later developed the specialized version for first-year students, which they have taught 4 times as the instructor of record. The EdFilm curriculum incorporates a range of popular media materials, including Hollywood movies that revolve around high school life such as Freedom Writers and The Hate U Give. These materials are used to analyze media content through an intersectional lens that considers aspects of race, gender, and social class. With an emphasis on social justice, the course introduces concepts like the myth of meritocracy, the white savior trope, dominant and counter narratives. Students learn how systemic inequality and structural oppression impact education, and how mass media operates as a system. We aim to help students develop an understanding of their own media socialization. As instructors, we share about our personal experiences and self-reflections on media socialization in class to humanize the classroom and model the type of self-reflection we expect of students. When sharing our personal self-reflections in class, we underscore that media messages affect us too, and as instructors, we are not somehow “above” the media’s influence.

Learning Objectives: Critical Media Literacy

Our instructional design and pedagogical approach are framed by the learning goals of Critical Media Literacy (CML). As we use the term, CML refers to the practice of analyzing media texts as social constructs that shape, and are shaped by, societal discourses, ideologies, systems of power, and cultural narratives (e.g., see Kellner & Share, 2005). CML education is not just about analyzing media but also about developing students as critical democratic citizens who challenge social injustice and take part in social change. CML pursues these goals by helping students “deconstruct the myths and take action to create counter-hegemonic media whereby students become the subjects and name their world” (Share, 2009, p.37).

To understand the relevance of CML to our current discussion of GenAI, we review two key perspectives within the media education movement: the protectionist and celebratory approaches. In the protectionist approach, mass media is framed as a dangerous and powerful force that students must be protected from through media literacy. On the contrary, the celebratory approach emphasizes the power of media technologies for self-expression and social change. Butler (2019) argues that media literacy education in the US can be defined through this protectionist – celebratory dichotomy; while the former is “a defensive position, both in philosophy and in its intention to develop armor against media influence” (p. 25), the latter “values and encourages youth participation in media as audiences and producers” (p. 25). The framework of CML transcends this dichotomy by incorporating the criticality of the protectionist stance along with an emphasis on media production for social change. As Kellner and Share (2005) describe: “Teaching critical media literacy involves occupation of a site above the dichotomy of fandom and censor (p.273).”

The protective and celebratory positions described above are resonant with mainstream responses to GenAI. For example, a celebratory position tends to frame new educational technologies as inherently positive and helpful – something to be embraced (uncritically), sometimes even overpromising about closing achievement gaps and providing more equal educational opportunities at a previously unknown scale. On the other hand, there are skeptics who caution against each new technology and circulate dire warnings about how it will suck the soul out of teaching and learning. New technology such as GenAI is automatically viewed with suspicion, a tool for cheating and academic dishonesty. In the EdFilm class, informed by CML, we maintain a stance toward mass media that is “neither fearfully protectionist nor blindly celebratory” (Share, 2009, p.22). We will adopt the same stance in our approach to GenAI. Just as we invite students to critically analyze media texts and the social contexts of media production – without demonizing or uncritically celebrating the media – we will engage students in a process of problem-posing to examine the uses, impacts, possibilities, and dangers of AI technologies. We will be transparent with students about these parallels, why we are doing it, and how this approach aligns to the course learning goals. In the next section, we describe in detail how we will operationalize this approach in our course.

Instructional Innovation: Integrating GenAI in the EdFilm Classroom

We will implement three changes to the EdFilm course to respond to the realities of GenAI: (1) intentional tone-setting; (2) in-class activities; (3) updated assessments and grading. These changes all reflect our intention to critically engage with GenAI the same way we engage with media technology, applying a CML framework.

Intentional Tone-Setting

In alignment with the CML framework, we will take a balanced approach to GenAI with our students. On one hand, we will critically examine it as a system, considering issues of power, profit, access, equity, authorship, and intellectual property. On the other hand, we will reject a distrustful tone that overlooks the existing conditions of students’ lives. Like any technology, AI can be used for ethical or unethical ends. Rather than demonize the technology as (solely) a cheating tool or celebrate it as a panacea for teaching and learning, we will invite students into critical examination of its uses, impacts, dangers, and possibilities. To convey this stance to students, we will intentionally set a tone that acknowledges the presence of GenAI and foster an environment where students can engage with this tool while adopting a critical perspective. The updated course syllabi contain references GenAI, such as this section from Author 1’s current syllabus:

The rise of Generative AI tools (such as ChatGPT, Microsoft Bing, etc.) has gained interest among numerous students. Developing proficiency in the appropriate utilization of GenAI tools is becoming an important skill. In this context, this class provides a safe space to explore those tools with attribution. This class values your experiences, and as your instructor, I support any tools that facilitate your learning process, if you genuinely show your interest and passion for learning, bring your voice, challenge your previous assumptions, and embrace new ideas.

In the EdFilm class, we frame media and technology as a system, making Gen AI tools relevant to our course content. I encourage you to bring your questions, concerns, or examples from other classes to discuss these tools critically, avoiding blind celebration or dismissal.

In-Class Activities

We will integrate the topic of GenAI into class discussions and activities. Just as we do with media technology, we will bring our own identities, experiences, and assumptions about GenAI into the conversation. For example, Author 1 uses ChatGPT for support with proofreading and editing, which significantly helps manage anxiety about academic writing as an international, first-gen graduate student. Author 2 initially brought a negative bias to GenAI, a stance likely influenced by their experiences as a white, upper-middle class, US citizen with a private liberal-arts college education. Through productive dialogue with Author 1 about GenAI, Author 2 developed a more nuanced understanding of AI as a tool with possibilities and limits. In sharing our own views and evolving feelings about AI, we will set a non-judgmental tone that encourages our students to reflect on and discern their own perspectives.

Additionally, through discussion prompt questions and the connections we will make to media literacy, we will push students to consider issues of social context, power, equity, and justice as they relate to AI technology. Specifically, in the course, we already pose critical questions about the media that encourage students to examine it as a system, such as: “How does media work? Who is in charge? Whose voices are amplified while some are silenced?” Along these same lines, we will pose questions about AI to encourage a critical evaluation. For example:

• What are the limits, possibilities, and uses of GenAI?
• Do you think using GenAI is ethical or unethical, and why?
• Who is behind AI technology? Who benefits from it? Who profits? Who is harmed?
• Why should you learn to do things (like write a college essay) if AI can do it for you? Is there a purpose to learning this skill? If so, what is it? What is the point of this education?
• Why are we discussing GenAI in this education class? What does it have to do with the field of education? For those of you planning to become teachers, how is this topic relevant to your professional training and future work?

Lastly, we are designing in-class group activities in which students will use AI tools to generate responses to assignments and essay questions, and then evaluate the output and present their critique to the class. If this activity goes well, we hope to repeat it in future class sessions, for example, by asking student groups to compare/contrast their AI-generated responses with a strong student-written response. In each case, we will follow this activity with a whole-class discussion to debrief, analyze, and connect their insights to course learning goals. We will use class time for this group activity before a major writing assignment is due, so that students are interacting not only with essay prompts and course materials but also critically analyzing what AI tools can and cannot offer. Importantly, after this activity, we will include time for students in class to workshop their own individual responses to essay prompts and homework assignments. This innovation builds on pedagogical strategies we already use in our class, such as building in class time for students to workshop essay responses in groups but extends them to incorporate a critical discussion of AI tools.

Updated Assessments and Grading

In addition to tone-setting and in-class discussions, we updated our major course assessments and grading formula, while adding clear guidelines about proper and improper uses of GenAI. These modifications build from practices that we have already been using. For example, the course has always been heavy on student writing. Students complete weekly written reflections (called note-catchers) as well as three take-home essays that address cross-cutting themes from a section of the course (about 4 weeks of course material). We explicitly frame the essays as a learning tool rather than (simply) a grading artifact, emphasizing process over product. Essay prompts build from weekly note-catchers and discussion prompts, and they invite students to reflect on their own personal experiences and connect course materials to their own lives. Although the three essays combined never account for more than 50% of a students’ total course grade, their relative weight in the grading system provided an incentive for academic dishonesty among students who had not consistently kept up with course material or class.

The major modification we will make is not in the substance of these assignments, but how they are framed and packaged. Instead of submitting a take-home essay at the end of each four-week section, students will assemble a learning portfolio that provides multiple forms of evidence of their engagement and learning, as well as a written self-assessment. To complete the portfolio, students will assemble their in-class notes, weekly note-catchers, and the take-home essay into one packet. They will add a self-assessment in which they reflect on how they engaged with course material and what they learned during that section of the course. The self-assessment will include space to discuss their use of GenAI as a support tool, if applicable, and insights about how their use of it helped or hurt their learning. Portfolios will be graded holistically, rather than breaking down each component with a specific number of points. We will communicate to students that we are evaluating the portfolio for three items: evidence of engagement with course materials, evidence of participation in class activities, and evidence of understanding the course content. The portfolio should provide ample evidence of all three components, but it might look different for each student.

In addition, all assignment instructions include clear language about appropriate use and attribution of AI tools. Here is an excerpt from the Portfolio assignment description: If you use any AI tools to assist you with this course, I expect you to acknowledge their use in writing, regardless of how you employ them. You must include the following information in your portfolios:

1) The entire interaction (screenshots), highlighting the most relevant sections.

2) An explanation of how the AI tools were used (e.g., generating ideas, crafting expressions, composing textual components, etc.).

3) The reason why you used the tools.

4) What you learned from this experience.

Failure to mention the use of AI tools will result in an automatic score of 0 for the assignment. Please do not hesitate to contact me well in advance of assignment deadlines if you plan to utilize generative AI tools; I am more than willing to provide guidance on their effective use.^[1]

We believe replacing take-home essays with portfolios will encourage students to take ownership of their learning and develop metacognitive skills. We also believe it will be a disincentive to submitting GenAI-produced essays, because it de-centers the essay as the focal point of grading. The essay is still included, but instructors will read it together with other examples of student work–providing a more holistic view of students’ participation, engagement with course materials, and conceptual understanding. By providing space in the self-assessment for students to share and reflect on their use of AI tools, we will invite them to experiment and critically assess its limits, possibilities, and implications for learning.

Discussion & Implications

In the summer of 2020, we completely overhauled the EdFilm course design to ensure the quality of our instruction in an online space due to the pandemic (see Söken & Nygreen, 2021). Now, the summer of 2023 marks another significant revision to the course in response to the rise of GenAI. This serves as a reminder of our responsibility as educators to continuously revise, refine, and improve our pedagogical practices based on contextual, political, and social changes.

Based on this experience, we identify five key principles that could be useful to others revising courses for this new social context.

• Start with what works well. The foremost lesson we draw is the importance of focusing on what has been effective and building on that. For instance, our in-class discussions and group work have proven to be effective. Consequently, we designated more time within class for students to engage in these activities, even incorporating some elements into their writing assignments.
• Be transparent about your GenAI policy. As we explained earlier in the paper, we have adopted a balanced approach. We adhere to the principles of critical pedagogy and perceive education as a liberating experience. Outright bans on GenAI tools or policing students’ use of them do not align with our perspective. Our intention is to establish an environment where students can share their tool experiences, develop a critical understanding of their functioning, and utilize them as supportive tools. This is achievable through a well-defined policy about the tools, along with creating a classroom space for discussing them, without promoting or dismissing them.
• Learning is a process, not a product. While it might be a common cliché in the field of education, it remains a foundational principle. This perspective is central to our role as educators and continues to guide our practices. It starts with delivering a clear message to students about this concept, which is then implemented through assignments and grading methods. To underscore this viewpoint, we have introduced a new assignment in EdFilm—portfolios—where students compile smaller assignments, assess their own performance, and share their interactions with GenAI tools.
• Bring your voice into the classroom. Our pedagogical stance encourages us to be authentic human beings in the classroom, reflecting the real-life challenges and struggles of educators. In the EdFilm context, we maintain transparency about our own learning experiences and the obstacles we encounter. In alignment with this transparency, we are also willing to share our evolving perspectives on GenAI tools, which remain dynamic and ever evolving, as well as our interactions with them.
• Foster a space for students’ life experiences. The EdFilm class delves into how popular culture influences and mirrors power dynamics, ideology, and oppression. Beyond this, we firmly believe that every learning environment possesses a degree of flexibility to embrace students’ identities and experiences. Given the presence of GenAI tools and concerns about incomplete homework, it becomes imperative for educators to design activities that invite students to integrate their out-of-classroom experiences into our classroom conversations.

Conclusion

In this chapter, we shared our experience as college instructors in the context of GenAI, and described instructional innovations we are implementing to respond to this new context. In our class, Education & Film, we use the lens of Critical Media Literacy (CML) as a way to contextualize, examine, and critique mass media technologies. We have argued that the CML framework provides a useful model for teaching with and about GenAI, and we have described modifications that we are making to our class in order to operationalize this. Through these adaptations, we aim to ensure that students engage in meaningful learning experiences while navigating the complexities of the college environment during the GenAI era.

Like all new technologies, including media technology, we believe GenAI is neither a panacea nor an existential threat; it is a tool that can be used (and abused) for many different purposes. Our goal as educators is to involve students in critically analyzing this tool for themselves, while centering questions about power, justice, and equity. We hope this chapter will provide a fresh lens for fellow educators to critically engage with the realm of GenAI.

References

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About the authors

name: Ali Söken

institution: University of Massachusetts Amherst

Ali Söken is a Ph.D. student in Teacher Education and Curriculum Studies at UMass Amherst. My research focuses on critical media literacy and teacher education, and in my dissertation, I am examining how future teachers learn media literacy and how to teach it effectively. I am also an instructor teaching a course on Education and Film that explores issues of race, gender, and social class. In the past, I have worked on various projects in the fields of computational thinking, scientific literacy, and technology integration, and I have taught a range of courses such as Theories of Learning, Introduction to Special Education, and Education and Film. Prior to beginning my Ph.D. program, I worked as an instructional designer for an e-learning company and as a project manager for a non-governmental organization.

name: Kysa Nygreen

institution: University of Massachusetts Amherst

Kysa Nygreen (she, her, hers) is an Associate Professor in the Department of Teacher Education and Curriculum Studies at University of Massachusetts Amherst. Her research and teaching focus on racial and social justice in education, critical pedagogy, community-engaged research, and ethnographic methods. She is the author of These Kids: Identity, Agency, and Social Justice at a Last Chance High School (University of Chicago Press).