When AI meets literature: Rethinking the classroom in the age of algorithms

A student opening Hamlet today does not have to spend hours working through its tangled plot before finding an explanation. An AI tool can produce a synopsis, list the play's major themes, unpack its symbols and even draft an essay on the tragedy — all within seconds.

That speed is transforming higher education. Tasks that once required days of reading, note-taking and comparison can now be compressed into a few prompts. For literature departments, however, the change raises a question more difficult than whether the technology should be allowed in the classroom: What happens to literary education when a machine can generate an answer before a student has learned how to ask the question?

AI is already in the classroom

Artificial intelligence is no longer a distant prospect for universities. It is already being used in research, teaching, academic writing, student support and digital humanities projects. Rather than replacing literary scholarship, AI can help researchers search large archives, identify patterns across thousands of texts and compare how language, style or themes change across periods and authors.

This work builds on the growth of digital humanities, a field that combines computational methods with traditional humanistic inquiry. Over the past two decades, scholars have increasingly used digital tools to study bodies of literature too large to examine manually. The technology can reveal connections that might otherwise remain hidden, but the purpose remains human: to understand culture, history, language and experience.

UNESCO's 2023 guidance on generative AI in education and research argues that, when used responsibly, the technology can support personalised learning, accelerate research and widen access to educational resources. Universities including Harvard, Oxford, Stanford and King's College London have also explored projects at the intersection of AI and the humanities, reflecting a growing recognition that graduates will need both disciplinary knowledge and digital literacy.

Students, meanwhile, have already moved faster than many institutions. The Digital Education Council's 2024 Global AI Student Survey found that 86% of university students used AI in their studies, often to generate ideas, summarise texts, improve language and organise research. The debate, therefore, is no longer about whether students will use AI. It is about whether universities can teach them to use it critically, ethically and transparently.

The machine can scan. But can it read?

Literature occupies a distinctive place in this debate because it is concerned less with retrieving information than with interpreting meaning. An algorithm can identify repeated words, classify themes and summarise a plot at remarkable speed. What it cannot do is experience the emotional, historical and cultural pressures that shape both a text and its readers.

Generative AI can summarise Hamlet, point to recurring symbols in The Great Gatsby, compare the prose of Jane Austen and Virginia Woolf, or produce a passable Shakespearean sonnet. These are impressive advances in language processing. But literary interpretation has never been only a matter of pattern recognition.

Consider Hamlet's most famous question: 'To be, or not to be.' AI can explain that the line is connected to mortality, despair and indecision. Yet its meaning is not fixed by a list of themes. A young reader encountering grief, a scholar studying political power and an audience watching the play during a period of national crisis may each hear something different. Meaning changes through the encounter between text, history and lived experience.

That is why the question of whether a machine can 'read' literature is not merely technological. It goes to the heart of what literary education is meant to cultivate: patience with ambiguity, attention to language, awareness of context and the ability to defend one interpretation while remaining open to another.

When convenience replaces engagement

The greatest risk is not that AI will become better at summarising books. It is that students may begin to treat the summary as a substitute for the book.

If learners routinely use AI to produce literary analyses, essays or interpretations without wrestling with the texts themselves, universities risk turning literature into a collection of facts: plots to memorise, metaphors to identify and themes to repeat. The discipline's deeper value lies elsewhere. It teaches students to question assumptions, weigh competing readings, recognise complexity and examine how language can conceal as much as it reveals.

Those habits are formed through engagement, not automation. A student who asks AI for the meaning of a poem may receive a fluent answer. But fluency is not the same as judgement, and an interpretation generated instantly does not show how the reader arrived there.

A tool, not a substitute

Rejecting AI altogether would be equally misguided. Used with clear limits, it can be an effective academic tool. Students can use it to explore unfamiliar historical contexts, compare critical perspectives, organise research notes or overcome language barriers. Researchers can use it to accelerate literature reviews, search large textual databases and support digital humanities projects that would take years to complete by hand.

The challenge is to design assignments that make thinking visible. Teachers might ask students to critique an AI-generated interpretation, verify its evidence, identify what it overlooks or compare it with their own close reading. Oral examinations, annotated drafts, reflective notes and classroom discussions can also help distinguish genuine understanding from polished machine-produced prose.

Universities will also need clear rules. Students should know when AI use is permitted, how it must be disclosed and where it becomes academic misconduct. Teachers, in turn, need training not only to detect misuse but to build assessments that reward analysis, originality and intellectual process rather than formulaic answers.

The choice before Bangladesh's universities

For universities in Bangladesh, the arrival of generative AI should prompt more than a debate over plagiarism software. It is an opportunity to reconsider how literature is taught and assessed.

Departments can combine digital literacy with the traditional strengths of literary study. Students should learn how AI systems generate responses, where bias and fabricated information can enter those responses, and why machine-produced interpretations still require verification. At the same time, courses must protect the slow, demanding practices that technology cannot replace: close reading, sustained argument, historical inquiry and conversation across different points of view.

AI may be able to imitate the language of Shakespeare, Austen or Woolf. It can map patterns across libraries and produce an essay-shaped answer in seconds. But it cannot bring a life to a text, feel the unease of an unresolved ending or recognise why the same line can wound one reader and comfort another.

The future of literary studies, then, does not depend on defeating the machine. It depends on using the machine without surrendering the distinctly human capacities that make literature worth reading in the first place.

Eshraful Parvez is the President of the Band Association of Pabna University of Science and Technology and a graduate in English Literature.

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.