In the ever-evolving landscape of education, where artificial intelligence (AI) increasingly shapes our teaching methodologies, I found an unexpected ally in Mary Shelley’s Frankenstein. This 19th-century novel became a compass this semester as my senior English class at The Miami Valley School (OH) navigated the complex terrain of AI integration. Our journey through Shelley’s timeless narrative about a young scientist who becomes obsessed with creating life not only deepened our understanding of the text, which raises questions about the ethical implications of scientific exploration but provided a critical lens to examine creation and responsibility issues related to AI.
Shelley’s novel provided a powerful example for grappling with the ethical implications of modern technology, exploring fundamental questions about creation, responsibility, and the nature of intelligence. We learned that AI’s true power lies in its capacity to augment and inspire human thought—not replace it.
Two years into this AI transformation, educators must admit that the “prohibit and restrict” approach isn’t working. Schools need to trust students with these tools, guide them in their ethical use, and empower them to think critically about their capabilities and limitations. I believe the key is to balance mastering foundational skills and learning AI-related competencies. This dual focus prepares students for a future where human-AI collaboration will likely be the norm.
Building Trust in the AI-Integrated Classroom
After reading the novel and before turning to discussion, I wanted to create an atmosphere conducive to real learning and growth, where students would feel comfortable openly discussing their use of AI tools. To cultivate this trust, I implemented several ideals:
- Being transparent about AI’s role: I was upfront about how AI fit into our learning goals, setting clear guidelines and explaining the reasoning behind them.
- Showing vulnerability: I shared my own learning curve with AI, encouraging students to be open about their challenges and discoveries.
- Encouraging conversations: When students shared how they’d used AI, we discussed what worked, what didn’t, and how they approached it. This normalized AI use and turned it into a shared learning experience.
- Celebrating the process: I recognized students’ efforts in applying AI, reinforcing that it was a tool for growth and experimentation—not just a quick fix.
- Modeling responsible AI use: I regularly demonstrated how I incorporated AI in my own work, showing students how to use these tools thoughtfully and ethically.
Frankenstein: A Mirror to Our AI Moment
As we studied Frankenstein, the parallels between Shelley’s narrative and our current AI landscape became strikingly apparent. The pivotal moment when Dr. Frankenstein watches his creation come to life resonated deeply with our class discussions about the current state of AI development.
We explored how Dr. Frankenstein’s immediate rejection of his creation mirrored some current attitudes toward AI—the instinct to abandon or stringently restrict it out of fear or misunderstanding. “But we can’t afford to flee the room like Dr. Frankenstein did,” one student pointed out. “AI is already deeply embedded in our world. We need to engage with it responsibly.”
This realization became a guiding principle for our class: the importance of taking responsibility for the technologies we create and use, rather than abandoning them when they challenge our preconceptions or comfort zones. We also embraced the idea that AI—no matter how advanced—fundamentally is a tool that we can responsibly and ethically leverage.
AI as a Literary Analysis Tool
With this mindset, we embarked on integrating AI tools into our study of Frankenstein. Our goal was to use AI tools not as shortcuts, but as a means to deepen our analysis and expand our critical thinking. I introduced the students to several AI-powered tools designed to assist in literary analysis:
- Sentiment analysis tools to explore character emotions
- Natural Language Processing (NLP) models for theme identification
- Text generation models to explore alternative narratives or interpretations
One student used sentiment analysis to track the emotional journey of Frankenstein’s creature throughout the novel. This approach revealed patterns, like the creature’s growing despair and rage in response to repeated rejections. By analyzing the creature’s dialogue and monologues, the student pinpointed shifts from curiosity and hope to bitterness and vengeance, offering a nuanced view of his psychological development. These insights demonstrated how sentiment analysis can provide a data-driven perspective on character evolution, highlighting emotional arcs that deepen the reader’s engagement.
Another group used an NLP model to identify recurring themes across different characters’ narratives. The model analyzed how themes like isolation, ambition, and responsibility appeared in characters like Dr. Frankenstein and the creature. This thematic analysis revealed multiple pathways to understanding the novel’s complex moral landscape. For instance, the students found that Dr. Frankenstein’s ambition and isolation mirrored the creature’s, suggesting parallel arcs. Rather than replacing traditional analysis, the NLP model showed how various interpretations of key themes can lead to new insights about humanity and responsibility.
Perhaps most intriguing, a team of students used a text generation model to explore potential alternative endings to the novel. After inputting the text, they generated variations on Shelley’s conclusion, such as one where the creature finds redemption or another where he exacts full revenge on Victor’s family. The students critically analyzed how these AI-generated endings aligned with or diverged from Shelley’s original themes, sparking discussions about authorial intent, narrative structure, and the multiple ways a story can reach a satisfying conclusion. This exercise highlighted the flexibility of Shelley’s framework and the potential for AI to expand literary interpretation beyond a single canonical ending.
Literature’s Enduring Relevance in the AI Age
As the semester progressed, so did my students. Their analyses became more sophisticated, demonstrating increased confidence in tackling complex themes and a greater willingness to explore unconventional interpretations.
More important, their approach to AI became more nuanced and critical. They learned to view AI outputs as starting points for further inquiry rather than definitive answers. They began to ask probing questions: What biases might be embedded in this AI tool? How might the training data influence its interpretations? How can we use this tool to enhance our understanding while maintaining our critical thinking skills?
By learning to work alongside AI, rather than using its outputs, students were preparing for a future where human-AI collaboration likely will be the norm across many fields. By fostering open dialogue and experimentation, as in this Frankenstein unit, students can become active, thoughtful collaborators with AI technology. Next we will read Ian McEwan’s Machines Like Me.
As educators, we stand at a crossroads similar to Dr. Frankenstein’s. Will we flee from this creation out of fear or embrace the responsibility of guiding these tools for good? Our experience suggests that the latter path, while challenging, offers immense rewards. By teaching students to engage critically and ethically with AI through classic literature, we prepare them for thoughtful participation in shaping our technological society.