Between Human Creativity and Machine Intelligence: A Digital Humanities Exploration

Exploring Digital Humanities: Machines, Poetry, CLiC, Voyant and Orange

Introduction

Digital Humanities is an emerging field that connects traditional humanities subjects such as literature, language, history, and culture with digital technology. As an M.A. English student, I usually study literary texts through close reading, interpretation, and critical analysis. However, digital tools provide a different way of understanding literature. They help us examine large amounts of textual data, identify patterns, compare texts, and represent information visually.

As part of this activity, I explored different resources and tools related to Digital Humanities. These included the discussion “What If Machines Write Poems?”, Oscar Schwartz’s TED Talk “Can a Computer Write Poetry?”, the Human or Computer? / Bot or Not poetry test, the CLiC Dickens Project, the CLiC Activity Book, Voyant Tools, and Orange. These activities helped me understand how technology can support literary studies. At the same time, they made me think about creativity, authorship, originality, and the relationship between humans and machines.

1. Can a Computer Write Poetry?

One of the most interesting questions I encountered during this activity was: “Can a computer write poetry?” Before exploring this topic, I thought poetry was mainly connected with human emotions, imagination, personal experience, and creativity. I believed that computers could imitate poetry but could not truly create it.

Oscar Schwartz’s TED Talk challenged this assumption. The talk shows that the question of computer-generated poetry is not simply about whether a machine can produce a poem. It also raises deeper questions about creativity, intelligence, authorship, and what it means to be human.

At the beginning of the talk, Schwartz presents poems and asks the audience to identify whether they were written by a human or a computer. The task is not as easy as expected. This experiment challenges our assumptions about human creativity because a poem can appear emotional, meaningful, or unusual without immediately revealing who or what produced it.

Schwartz connects this idea with Alan Turing’s Turing Test. The Turing Test raises the question of whether a machine can communicate in a way that makes it difficult for a person to distinguish it from a human. Schwartz applies a similar idea to poetry through the “Bot or Not” test, where readers try to identify whether poems were written by humans or computers.

This activity made me question my own understanding of poetry. A computer can analyse language, recognise patterns, and generate new combinations of words. As a result, computer-generated poems can sometimes appear surprisingly similar to human poetry.

However, I also understood an important difference between producing a poem-like text and having a human experience behind the poem. A human poet may write about love, loss, nature, loneliness, memory, or society based on personal experiences and emotions. A machine does not experience these things in the same way. It works through patterns and information.

At the same time, this does not mean that computer-generated poetry is meaningless. It can create new possibilities for creativity and literary experimentation. It also encourages us to rethink traditional ideas about authorship, originality, and creativity.

2. My Experience with the Human or Computer Poetry Test

The “Human or Computer?” poetry test was another interesting part of the activity. The purpose was to identify whether a poem was written by a human or generated by a computer.

While taking the test, I tried to identify the author by examining the language, structure, imagery, emotions, and overall meaning of the poems. Sometimes I felt confident that a poem was written by a human because it seemed natural and emotional. In other cases, I was unsure because the computer-generated poem also appeared meaningful and well-structured.

This experience showed me that it can be difficult to distinguish between human and computer-generated writing simply by reading a short poem. A computer can reproduce poetic forms, vocabulary, rhythm, and familiar images effectively.

The test also made me realise that my judgment is influenced by my expectations. I often associate meaningful writing with human creativity. However, digital technology challenges this assumption.

The experience was therefore more than a simple test. It became a way of examining my own understanding of authorship and creativity. I learned that we should not judge a literary text only by whether it appears human or machine-generated. We should also consider its language, context, structure, purpose, and cultural significance.

3. Creating a Poem and Experiencing a Poem

Another important idea I understood from Schwartz’s talk is the difference between creating a poem and experiencing a poem.

When I read a poem, I respond to its words, images, rhythm, and meaning. If I later discover that the poem was generated by a computer, my response may change. This raises an important question: Does knowing the author change the way we experience literature?

If I believe that a poem was written by a human being, I may imagine that the poet experienced the emotions expressed in it. If I discover that it was generated by a computer, I may begin to question whether the poem contains genuine emotion or only a pattern of language.

Therefore, authorship can influence interpretation. This is especially important in literary studies because we often connect a text with the author’s experiences, intentions, historical context, and cultural background.

However, the meaning of a literary text is also created through the reader’s response. Even if a computer produces a poem, a human reader can still interpret it, discuss it, and find meaning in it.

4. CLiC – Dickens Project

The next activity introduced me to CLiC, a digital tool designed for the study of literary texts. The CLiC Dickens Project focuses particularly on the works of Charles Dickens and provides ways to investigate literary texts through digital methods.

Traditionally, when studying a novel, I would read the text carefully and make notes about characters, themes, language, and narrative techniques. CLiC provides another method of analysis. It allows students and researchers to examine words, phrases, and patterns across texts.

One important thing I learned from CLiC is that computers can help us identify patterns that may not be immediately visible through ordinary reading. For example, if a particular word or phrase appears frequently in a novel, a digital tool can help us locate and compare those occurrences quickly.

However, the computer does not provide the final interpretation. It provides evidence that the researcher must understand and interpret. In this way, CLiC can support traditional close reading.

The Dickens Project helped me understand how Digital Humanities can be used to study authors and literary texts systematically. Instead of focusing on only one passage, we can examine patterns across larger sections of a text.

5. CLiC Activity Book

The CLiC Activity Book provided a practical understanding of digital literary analysis. It demonstrated how activities can be used to explore literary texts and language patterns.

I found this useful because it changed the way I think about reading literature. Literary analysis is often based on interpretation and close reading, but digital activities provide another layer by helping us collect and examine textual evidence.

Through these activities, I understood concepts such as frequency, repetition, collocation, and patterns of language. Such concepts can be useful when studying novels and other large texts.

One of the major learning outcomes from the CLiC activities was that digital analysis and traditional literary analysis can work together. A computer can help us find patterns, but the human researcher must explain why those patterns are important.

Therefore, Digital Humanities is not simply about using computers. It is about using technology to ask new questions and develop new methods of studying humanities subjects.

6. Exploring Voyant Tools

Another important tool introduced in this activity was Voyant Tools. It is a web-based text analysis environment that can be used to examine literary and other textual materials.

I found Voyant interesting because it presents textual information visually. Instead of reading a long text only from beginning to end, we can use visualisations to examine word frequency and patterns.

One useful feature is the ability to identify frequently occurring words. This can provide an initial idea about the major vocabulary of a text. However, frequency alone cannot explain the complete meaning of a literary work. A word may occur frequently because of its grammatical function or the author’s style.

Therefore, I learned that Voyant should be used as a starting point for interpretation rather than a replacement for reading. It can help us discover patterns, after which we can return to the original text and interpret those patterns in context.

The visual nature of Voyant also made textual analysis more engaging. Information that might take considerable time to identify manually can be represented quickly through digital visualisation.

7. Exploring Orange

The activity also introduced me to Orange, a tool associated with data analysis and visualisation. At first, Orange seemed different from the literary tools that I normally use. However, I understood that it can be useful for organising, processing, and representing information.

Orange helped me understand that Digital Humanities involves not only reading texts but also working with data. For an English student, this is a new way of thinking.

Literature is usually associated with imagination, interpretation, language, and human experience, while data analysis is often associated with science and technology. Digital Humanities brings these areas together.

My experience with Orange helped me understand the importance of interdisciplinary learning. Literary studies can benefit from methods developed in areas such as computer science, data analysis, and visualisation.

8. What I Learned from These Activities

These activities changed my understanding of Digital Humanities. Before doing them, I mainly thought of literature as something that should be interpreted through traditional reading and critical theory. Now I understand that digital tools can provide additional evidence and new perspectives.

The poetry activities made me think about artificial intelligence, authorship, originality, creativity, and human identity. The Human or Computer test showed me that it is not always easy to identify the creator of a poem. CLiC helped me understand how digital tools can identify patterns in literary texts. The CLiC Activity Book provided practical experience with digital analysis. Voyant helped me understand textual patterns through visualisation, while Orange introduced me to the relationship between data analysis and humanities research.

Another important lesson was that technology does not necessarily replace human beings. Instead, digital tools can work alongside human interpretation. A computer can identify a pattern, but a human researcher must decide what that pattern means.

I also learned that digital results should not be accepted blindly. We need to understand the limitations of digital tools and consider the context of the literary text. Technology can provide evidence, but critical thinking remains essential.

Conclusion

My experience with these Digital Humanities activities was informative and thought-provoking. The discussion of machine-generated poetry and Oscar Schwartz’s TED Talk challenged my understanding of creativity and authorship. The Human or Computer test showed me how difficult it can be to distinguish between human and computer-generated writing. CLiC introduced me to digital approaches to literary texts, while the CLiC Activity Book demonstrated how these approaches can be used practically. Voyant helped me understand textual patterns through visualisation, and Orange introduced me to the relationship between data analysis and humanities research.

The most important learning outcome for me is that the question “Can a computer write poetry?” cannot be answered simply with “yes” or “no.” Computers can generate poems that readers may mistake for human writing, but this raises deeper questions about creativity, intelligence, authorship, and humanity.

Overall, these activities helped me understand that technology and literature are not separate fields. They can work together to create new methods of research and interpretation. As an English student, I believe Digital Humanities can make literary study more analytical, interactive, and interdisciplinary.

The most important thing I learned is that digital tools should support human thinking rather than replace it. Technology can help us discover patterns and evidence, but interpretation, critical thinking, and human understanding remain essential. Digital Humanities therefore provides a valuable bridge between traditional literary studies and the rapidly changing digital world.