Introduction
In the rapidly evolving landscape of higher education, artificial intelligence (AI) tools such as ChatGPT have become ubiquitous, prompting debates about their impact on learning, writing, and critical thinking. As a student in Composition 1 (Comp 1), I have directly encountered these tools while navigating assignments that require original arguments and source integration. The course theme, as explored in assigned readings, highlights concerns that AI may undermine the effort and independent thought essential for genuine education. Authors like Daniel Cryer and Gary Smith with Jeffrey Funk argue that AI shifts ethical burdens onto students and fails to foster true critical thinking, potentially weakening educational outcomes. However, drawing from my personal experiences, I contend that AI does not inherently erode learning; rather, when used responsibly as a supplementary aid for brainstorming and organization, it can enhance critical thinking and writing skills. This essay argues that students should adopt AI as a tool to support, not replace, personal effort, thereby addressing the risks outlined in the sources while leveraging its benefits. To support this, I will examine the burdens AI places on students, its limitations in promoting critical thinking, and my own observations from using AI in Comp 1 assignments. This argument is informed by Cryer’s (2023) discussion of student responsibility and Smith and Funk’s (2023) critique of AI’s reasoning flaws, supplemented by an additional source on AI integration in education. By balancing these perspectives with primary evidence from my experiences, I aim to demonstrate how responsible AI use can align with educational goals, particularly for undergraduate students facing inconsistent guidelines.
The Ethical Burden of AI on Students
One of the primary challenges posed by AI in education is the increased responsibility it places on students to make ethical decisions, often without clear institutional support. In his article, Cryer (2023) describes this as “responsibilization,” where students must independently navigate the temptation to use AI for completing assignments, such as generating entire essays. He illustrates this with a scenario of a student alone with her laptop, weighing whether to rely on AI or engage in the writing process herself, emphasizing that “students now bear the lion’s share” of maintaining academic integrity amid unreliable detection tools (Cryer, 2023). This pressure is compounded by inconsistent policies across courses, as Warner (2023) notes in his piece, where professors vary in their approaches—some banning AI outright, others permitting it for ideation, and still others requiring disclosure. Such variability leaves students confused, forcing them to guess acceptable practices and risking unintentional violations.
From my personal experience in Comp 1, this burden resonates deeply. During a recent summary assignment, I considered using ChatGPT to draft an outline of an assigned reading. The course syllabus provided vague guidelines, stating only that “AI should not replace original work,” without specifics on what constitutes replacement. Sitting in my dorm room, much like Cryer’s hypothetical student, I felt overwhelmed by the decision. Would generating bullet points count as cheating, or was it a legitimate brainstorming aid? Ultimately, I chose to use AI minimally for initial ideas but wrote the summary myself, which required me to critically engage with the text. This experience confirmed Cryer’s point: without consistent guidance, AI creates an ethical minefield that can distract from learning. However, it also highlighted a positive aspect—by deciding to limit AI’s role, I reinforced my own accountability, arguably strengthening my ethical reasoning skills. Warner (2023) supports this by suggesting that mixed messages from educators exacerbate confusion, but in my case, the ambiguity compelled me to reflect more deeply on the purpose of the assignment, transforming a potential risk into an opportunity for growth.
Furthermore, this burden extends beyond ethics to practical learning implications. If students succumb to over-reliance, they may miss the “struggle” that Cryer (2023) implies is crucial for development. In my observation, peers who fully delegated tasks to AI often produced superficial work, lacking the depth that comes from personal revision. This aligns with broader educational concerns, where the absence of effort undermines the foundational skills Comp 1 aims to build, such as crafting arguments from scratch.
AI’s Limitations in Fostering Critical Thinking
Beyond ethical pressures, AI’s inability to perform genuine critical thinking poses a significant risk to educational quality, as argued by Smith and Funk (2023). They assert that large language models lack understanding of meaning, merely regurgitating patterns from data without causal reasoning. For instance, they cite examples where AI confidently outputs absurd errors, like an exaggerated insurance return of “11,878 percent,” demonstrating that “AI systems do not know the meaning of any of the words they input and output” (Smith and Funk, 2023). This flaw means students relying on AI for analysis may bypass the mental work needed for higher-order skills, such as evaluating evidence or drawing inferences—core elements of Comp 1.
My own encounters with AI underscore these limitations. In preparing for a class discussion on AI’s role in writing, I prompted ChatGPT to analyze a source’s argument on critical thinking. The response was grammatically polished but factually shallow; it paraphrased the text without addressing underlying assumptions or contradictions, such as how AI might simulate but not replicate human insight. This mirrored Smith and Funk’s (2023) critique, as the AI failed to engage in causal reasoning— it could not explain why certain AI outputs mislead users, only restate surface-level facts. Observing this, I realized that using AI uncritically could have led me to accept flawed interpretations, weakening my analytical abilities. Instead, by contrasting the AI’s output with my manual analysis, I developed a deeper understanding, identifying gaps that the tool overlooked. This personal observation contradicts a purely negative view, suggesting that AI’s shortcomings can serve as teaching moments, prompting students to apply critical thinking to evaluate the tool itself.
Moreover, Smith and Funk (2023) emphasize that “real intelligence requires critical thinking and causal reasoning,” which AI cannot provide. In Comp 1, where assignments demand original theses supported by evidence, over-dependence on AI risks producing hollow arguments. For example, during an essay drafting session, a classmate shared an AI-generated paragraph that sounded sophisticated but lacked logical flow, echoing the authors’ examples of erroneous outputs. This not only flunked the “test” of critical thinking but also highlighted how AI can mask deficiencies rather than address them. However, in my experience, treating AI as a diagnostic tool—generating ideas and then critiquing them—has honed my skills, turning potential weaknesses into strengths.
Integrating AI Responsibly: Lessons from Personal Experience
Building on these critiques, my experiences suggest that AI can be integrated responsibly to support, rather than undermine, learning. In Comp 1, I conducted informal primary research by surveying five classmates about their AI use. All reported using tools like ChatGPT for outlining or grammar checks, but three admitted it sometimes reduced their effort, aligning with Cryer’s (2023) concerns about responsibilization. Yet, two described how AI helped them overcome writer’s block, allowing deeper engagement once started. This small-scale observation supports my argument: AI’s value lies in its supplementary role.
Personally, for this very essay, I used AI to generate an initial structure based on the course theme, but revised it extensively using my notes and reflections. This process not only saved time but forced me to evaluate the AI’s suggestions critically, enhancing my thesis development. Unlike the risks outlined by Smith and Funk (2023), this approach fostered causal reasoning—I had to determine why certain structures worked or failed. An additional source reinforces this: Ng et al. (2023) review AI’s trends in education, noting that when used for collaborative learning, it can improve outcomes by scaffolding skills without replacing them. They argue that AI supports “higher-order thinking” when integrated thoughtfully, which mirrors my experience (Ng et al., 2023).
Arguably, this responsible use addresses the course theme’s warnings. By limiting AI to preliminary tasks, students can mitigate ethical burdens and critical thinking gaps, as I did in Comp 1. However, constraints like time limitations and varying professor expectations, as Warner (2023) describes, require better guidelines to maximize benefits.
Conclusion
In summary, while AI introduces ethical burdens and critical thinking limitations, as evidenced by Cryer (2023) and Smith and Funk (2023), my personal experiences in Comp 1 demonstrate that responsible use can enhance rather than hinder learning. By employing AI for brainstorming and then applying personal effort, students can develop stronger arguments and skills, countering the risks highlighted in the sources. This original argument underscores the need for educators to provide clearer policies, enabling AI to serve as a tool for empowerment. Ultimately, the implications for education are profound: embracing AI thoughtfully preserves the struggle essential for growth, ensuring it reshapes learning positively. As undergraduates, we must navigate this terrain proactively, turning potential pitfalls into opportunities for authentic development.
(Word count: 1528, including references)
References
- Cryer, D. (2023) To Use AI or Not to Use AI? A Student’s Burden. Inside Higher Ed.
- Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., and Chu, S. K. W. (2023) A review of AI teaching and learning from 2000 to 2020: Issues and trends. Computers and Education: Artificial Intelligence, 4, 100118.
- Smith, G. and Funk, J. (2023) When It Comes to Critical Thinking, AI Flunks the Test. Chronicle of Higher Education.
- Warner, J. (2023) AI Meets Academia—Navigating the New Terrain. Inside Higher Ed.
