Discursive Resistance to Algorithmic and Curricular Governance: A Participatory Critical Discourse Analysis of Teacher Agency and Democratic Literacy in Undergraduate Classrooms

In classrooms around the world, the tools of teaching are changing. For decades, educators have navigated the tension between following a set curriculum and responding to the unique needs of their students. Today, a new force has joined that mix: algorithms. These are the invisible rules and automated suggestions built into the software that helps teachers plan lessons, grade work, and track progress. While these digital systems promise efficiency and consistency, they also bring a rigid logic that can squeeze out the messy, human conversation at the heart of learning. When a computer suggests the "correct" path for a lesson, or a policy demands a specific outcome, the space for students to question, debate, and think for themselves can shrink. This raises a vital question for the future of education: can teachers and students still talk freely and think critically when their classroom is guided by both government policies and artificial intelligence?

A team of researchers set out to answer this by looking inside six university classrooms across Pakistan, Oman, and Saudi Arabia. They focused on undergraduate literacy courses, where students learn to read, write, and analyze complex texts. The researchers were not just watching from the outside; they invited teachers and students to become partners in the investigation. They called this approach "participatory critical discourse analysis." In plain terms, this meant that instead of a researcher simply observing and judging the classroom, the teachers and students sat down together to listen to recordings of their own lessons. They examined the words they used, the questions they asked, and the ways the computer software influenced their choices. The goal was to see how people navigate the pressure of strict rules and automated suggestions while trying to keep their classrooms alive with genuine discussion.

The study involved twelve teachers and two hundred students over several months. The researchers gathered a wide range of materials, including official school documents, lesson plans generated by artificial intelligence, and hours of video and audio recordings of actual classes. They also held regular sessions where teachers and researchers reviewed these recordings together. During these meetings, they looked for moments where the strict, automated language of the curriculum or the software clashed with the natural flow of human conversation. They wanted to know if the teachers simply followed the script or if they found ways to bend it to allow for student voices.

What they found was that the influence of these external forces was undeniable. The language of the curriculum and the AI software was everywhere, filled with directives like "complete the activity," "correct answer," or "learning objective." These phrases set a tone of efficiency and compliance. However, the researchers discovered that teachers and students did not simply obey these commands. Instead, they developed a set of subtle, everyday strategies to reshape the conversation. They did not reject the rules or the technology; they worked around them.

One of the most common strategies was what the researchers called "collective agency framing." Instead of a teacher saying, "You must do this," they would say, "Let's decide together." This small shift in wording changed the power dynamic in the room. It turned a command into a shared decision, inviting students to take ownership of the learning process. Another strategy was "re-lexicalisation," which simply means swapping out stiff, official words for more open, conversational ones. A teacher might replace a rigid "curriculum requirement" with a "big question for today." This opened the door for students to explore different ideas rather than just hunting for a single right answer.

The study also observed how teachers handled the AI itself. Rather than treating the computer's suggestions as final truth, teachers used them as a starting point for debate. One teacher might say, "The AI suggests this explanation, but what other possibilities can we imagine?" This turned the software from an authority figure into a tool for critical thinking. Students responded by offering their own interpretations, challenging the machine's output, and bringing in their own life experiences. In one recorded moment, a student noted that a story's ending could mean something different than the software predicted, sparking a rich discussion that the algorithm alone could never have generated.

The researchers identified four main ways these groups negotiated the constraints of their environment. Beyond the collective decision-making and word-swapping, they used "interdiscursive subversion," which involved bringing in community stories and personal experiences to broaden the narrow view of a textbook. They also used "strategic parody," where they would make light of the rigid rules or the AI's limitations with humor, which lowered the tension and made it safe to talk back. These strategies were not random; they appeared consistently across all six universities, showing that teachers and students had developed a shared, intuitive way to protect the spirit of democratic learning even when the system tried to standardize it.

Crucially, the study showed that the act of analyzing these conversations together was just as important as the strategies themselves. When teachers listened to recordings of their own classes with the researchers, they often realized they were using more closed, directive language than they thought. One teacher admitted, "I thought I was encouraging discussion, but the transcripts showed that I often answered my own questions before students had time to think." This moment of self-awareness led to real change. After these reflection sessions, teachers began asking more open-ended questions, waiting longer for answers, and using the AI lesson plans more flexibly. They moved from simply following a script to using the script as a resource for a deeper dialogue.

From these findings, the team created a practical guide called a "Discursive Resistance Toolkit." This was not a list of new rules to follow, but a collection of the strategies the teachers had already discovered and refined. It included ways to frame questions, how to introduce personal stories, and how to critically compare AI suggestions with human ideas. The toolkit proved that teachers could maintain their professional judgment and keep their classrooms democratic without rejecting the technology or the policies they had to work within.

The implications of this work extend far beyond these six universities. The study suggests that the future of education does not have to be a choice between human creativity and technological efficiency. It shows that when teachers and students are given the space to reflect on their own interactions, they can find ways to keep the human element alive. They can use the tools of the modern world without letting those tools use them. By turning the classroom into a place where everyone, including the teacher, is a learner and a critic, education can remain a space for critical thinking and democratic participation. This is essential for building a society where people are not just efficient workers following algorithms, but engaged citizens capable of questioning, understanding, and shaping their world. The research confirms that even in a highly regulated and automated environment, the human capacity for dialogue and resistance remains a powerful force for change.