Reading Beyond the Page: Exploring Literature Through AI, CLiC and Voyant Tools
Digital Humanities has opened new possibilities for the study of literature by bringing together traditional literary interpretation and digital technologies. As part of the activities assigned by Dr. And professor Dilip Barad sir , I explored the relationship between literature, artificial intelligence, and digital tools through a series of practical activities involving AI, CLiC, and Voyant Tools. These activities encouraged me to look at literature not only through close reading but also through computational methods such as frequency analysis, concordance, visualisation, and distant reading. The discussion of Oscar Schwartz’s question, “Can a Computer Write Poetry?”, made me think critically about creativity, authorship, meaning, and the difference between producing language and understanding it. Similarly, using CLiC and Voyant Tools helped me discover patterns in literary texts that are not always immediately visible through traditional reading. Overall, these activities gave me a broader understanding of how technology can complement literary studies and helped me see Digital Humanities as a bridge between humanistic interpretation and computational analysis.
Oscar Schwartz asks a very interesting question: Can a computer actually write poetry, or can it only produce something that looks like poetry?
He explains that computers can be programmed to generate poems by learning patterns from existing poetry. The result can sometimes look surprisingly human. But the important question is not simply “Can a computer produce a poem?” It is:
Does a computer understand what it is creating?
The talk explores the difference between producing language and experiencing meaning.
Main ideas
1. Computers can generate poetry
A computer can analyse large amounts of poetry and learn patterns such as vocabulary, sentence structures, rhythm and poetic forms. It can then combine these patterns to create new poems.
2. A poem can look human even when a computer created it
Schwartz demonstrates that computer-generated poetry can sometimes be difficult for people to distinguish from poetry written by humans. This challenges our assumption that creativity automatically belongs only to humans.
3. The Turing Test is important
The talk connects this issue to Alan Turing's idea of testing machine intelligence. If a machine can communicate in a way that humans cannot distinguish from another human, we may begin to question whether the machine is intelligent.
But Schwartz asks whether this test is enough for creative works such as poetry.
4. Producing language
This is probably the most important idea for your class.
A computer may arrange words correctly without having personal experiences, emotions, memories, or intentions behind those words.
For example, a computer can produce a poem about sadness, but that doesn't necessarily mean the computer has experienced sadness.
5. Poetry is connected to human experience
Poetry isn't only about putting beautiful words together. Human poets write from experiences, emotions, memories, cultural backgrounds and particular intentions.
Therefore, Schwartz makes us question whether a machine-generated poem has the same kind of meaning as a poem created by a human.
The central question
The video is ultimately asking:
If a computer produces a poem that makes us feel something, does it matter whether a human or a machine wrote it?
There are two possible ways to look at it:
- Yes, it matters: Human creativity involves consciousness, experience, emotion and intention.
- Maybe it doesn't: If the poem communicates meaning and creates an emotional response in the reader, perhaps the origin of the poem is less important.
Why this video is important for Digital Humanities / AI
This video is useful for understanding the relationship between technology and literature. It shows that AI doesn't simply affect science or technology—it also challenges traditional ideas about authorship, creativity, originality and literary value.
A key takeaway is:
AI can imitate the patterns of human creativity, but the video makes us question whether imitation is the same thing as genuine creativity.
This is also why the video is often used in discussions of AI and literature/poetry.
In very simple words
Computer: “I can create a poem because I have learned patterns from thousands of poems.”
Human: “But do you understand what the poem means or why you are writing it?”
Schwartz: “That is exactly the question we need to think about.”
My Experience of the Human or Computer Test
As part of the activity, I took a test to identify whether the poems were written by a human or a computer. I read each poem carefully and made my choice based on the language, imagery, structure, and connection between ideas.
At first, I thought I could easily distinguish human writing from computer-generated writing. However, the test was more challenging than I expected. Some poems had emotional and natural-sounding language, while others had unusual combinations of words and ideas.
For this particular poem, I chose “A Machine,” but my answer was wrong. The result revealed that the poem was actually written by Thomas Kinder, a human. This surprised me because the poem has a very structured and polished style, and I initially thought it might have been generated by AI.
The test showed me that AI can sound human, and human writing can sometimes sound like AI. My score was 4 out of 6, which made me realise that it is not always possible to identify the author simply by reading the poem.
Overall, this experience was useful and interesting. It encouraged me to read poetry more carefully and critically. I learned that I should not judge a poem as AI-generated only because its language seems unusual or highly structured. The activity helped me understand the difficulty of distinguishing human creativity from computer-generated writing.
My Experience with CLiC: Distant Reading
My Experience of Using CLiC to Study “Chin”
Using CLiC to study the word “chin” was an interesting and eye-opening experience for me. I found that the word appeared much more frequently in Dickens’s novels than in Jane Austen’s works. By comparing the frequency of “chin” across different corpora, I understood how corpus tools can help us notice patterns that we might not notice through ordinary reading.
Looking at the concordance lines was especially useful because it showed me how Dickens used physical descriptions to create and develop his characters. The description of a character’s chin could suggest their appearance, personality, social class, or even their state of mind. I also learned that a simple body part can have a deeper role in characterisation.
This activity helped me understand that language choices are closely connected to literary meaning. Frequency counts gave me evidence, while the concordance examples helped me interpret that evidence. Overall, the activity gave me a new perspective on how body language and physical description contribute to characterisation in fiction.
Voyant Tools Analysis of The Importance of Being Earnest
I used Voyant Tools to analyse Oscar Wilde’s The Importance of Being Earnest. The word cloud shows that Jack, Algernon, Gwendolen, Lady, and Cecily are among the most frequent words, highlighting the importance of these characters. The corpus contains 9,945 words and 1,669 unique word forms. The Trends graph shows how the prominence of characters changes across different sections of the play, while the Contexts tool helps examine how particular words are used in dialogue. Overall, Voyant shows that Wilde’s play is strongly character- and dialogue-driven, reflecting themes of identity, deception, relationships, and social performance.
Bubblelines Analysis of The Importance of Being Earnest
The Bubblelines visualization shows the frequency and connections of words and characters in Oscar Wilde’s The Importance of Being Earnest. Gwendolen, Algernon, Lane, and Jack appear prominently, indicating their importance in the text. The larger circles suggest words that occur more frequently or have stronger connections, while the smaller circles represent less frequent terms. The visualization also highlights recurring words such as “earnest,” “importance,” “married,” “love,” “lady,” and “young,” which connect to the play’s major concerns with marriage, identity, social class, and relationships. Overall, the Bubblelines visualization helps us see the structure and recurring vocabulary of the play through a digital approach.
Trends Analysis of The Importance of Being Earnest
The Trends visualization shows how the frequency of important characters and words changes across the text. Jack, Algernon, Gwendolen, Cecily, and Lady appear repeatedly throughout different sections, showing their continuing importance to the play. The larger bubbles indicate moments where these words occur more frequently, while the changing patterns show how the focus shifts between characters as the plot develops. This visualization demonstrates that Wilde’s play is strongly character- and dialogue-centred, with recurring attention to identity, relationships, marriage, and social expectations.
Trends Analysis of The Importance of Being Earnest
The Trends graph shows the distribution of Lady, Jack, Gwendolen, Algernon, and “844” across the ten sections of the text. Algernon and Jack appear strongly in several sections, while Gwendolen becomes especially prominent around section 5–6 and Lady is most noticeable around section 6. The graph shows that the focus shifts between characters as the play progresses. Overall, it demonstrates how Wilde’s plot develops through character interactions, relationships, and changing dramatic focus, especially around themes of identity, love, marriage, and social expectations.
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