The Algorithm of Morality: A Critical and Personal Reflection on Ethical Decision-Making, Human Values, Responsibility, and Artificial Intelligence through the Moral Machine Activity
This Blog task is assigned by the head of the Department of English (MKBU), Prof. and Dr. Dilip Barad as Lab Activity on DH. Here is the link to the professor's Blog for background reading: [Click here]
Introduction:
This blog reflects on my engagement with Digital Humanities and emerging forms of digital pedagogy, particularly through the Moral Machine Activity and Prof. Dr. Dilip Barad’s Faculty Development Programme, “A Pedagogical Shift from Text to Hypertext: Language & Literature to the Digital Natives.” The activities encouraged me to examine how technology is transforming not only teaching and learning but also our understanding of morality, authorship, literature, reading, and knowledge. The Moral Machine activity made me critically examine my own ethical preferences and biases, while the FDP sessions introduced me to hypertext, digital natives, generative literature, corpus-based analysis, and digital portfolios. Together, these experiences demonstrate how Digital Humanities creates a space where literary studies, technology, critical thinking, and self-reflection intersect.
Infographic of whole Blog:
My Experience and Learning Outcomes of the Moral Machine Activity
My Experience of the Moral Machine Activity
My experience with the Moral Machine Activity was much more challenging than I initially expected. At first, I considered it to be a simple interactive activity in which I only had to choose between two possible outcomes. However, after going through the thirteen moral dilemmas, I realised that every decision required me to make a judgement about life, responsibility, justice, law, and human value.
In each situation, I was asked to decide what a self-driving car should do when an accident was unavoidable. Sometimes I had to choose between protecting the passengers and saving pedestrians; in other situations, I had to decide between different groups of people, animals, children, elderly people, professionals, criminals, or people crossing the road legally or illegally. The screenshots of my activity show how the scenarios repeatedly forced me to make choices between two equally uncomfortable consequences.
What affected me most was that I could not make these decisions without thinking about the value I was unconsciously assigning to different lives. I initially wanted to follow a logical principle saving the greater number of lives but some situations challenged that principle. I began asking myself whether I should protect the passengers because they were already inside the vehicle, whether I should save pedestrians because they were more numerous, or whether I should consider the behaviour of people who were crossing the road illegally.
This made the activity personally uncomfortable but intellectually valuable. I realised that ethical decision-making is not simply about calculating numbers. Even when I tried to be rational, my emotions and assumptions influenced my choices.
Here is PDF of the screenshot of the Moral Machine Activity: Click Here
Here is the Result of the Moral Machine: Click Here
What My Results Revealed About My Moral Thinking
The most interesting part of the activity for me was seeing my personal result, because it allowed me to examine my own moral preferences instead of merely judging the choices I had made during individual scenarios.
My result showed that the cat was my “Most Saved Character,” while the man was my “Most Killed Character.” This immediately made me reflect on my own choices. I had not consciously entered the activity thinking that I would prioritise animals or that men would be more frequently sacrificed. Therefore, seeing this result made me recognise how different choices across separate situations can create a larger moral pattern that I may not notice while making individual decisions.
The result was especially interesting because I did not necessarily make these choices because I believed that a cat's life was more valuable than a human life. Rather, the individual circumstances presented in the dilemmas influenced my decisions. This taught me that context can strongly influence moral judgement.
I also noticed that my responses did not appear to be based strongly on physical characteristics such as fitness, gender, or age. On the preference scale, my positions for fitness, species, gender, and age were around the middle rather than at an extreme. This suggests to me that I was not consciously following a strict rule such as “always save the younger person” or “always save women” or “always save physically fit people.”
However, one preference stood out very strongly: saving more lives mattered a lot to me. This helps explain many of my choices. I often found myself thinking in terms of consequences and trying to minimise the overall loss of life. Looking back, I can see that my decision-making had a strong consequentialist tendency: when faced with two harmful outcomes, I was inclined to choose the option that appeared to produce the lesser overall harm.
Avoiding Intervention and the Question of Responsibility
Another significant discovery about myself was that avoiding intervention mattered a lot to me. This made me think about the difference between causing harm and allowing an existing situation to continue.
The activity made me ask myself: Is it morally different if the car actively changes its direction and causes someone's death compared with simply continuing forward?
I realised that I instinctively felt a difference between intervention and non-intervention. This is important because an autonomous vehicle may technically be able to intervene, but that does not automatically mean that intervention is morally uncomplicated. If the vehicle deliberately changes its path to avoid one group but consequently hits another, the question of responsibility becomes much more complicated.
This was one of the strongest philosophical lessons I gained from the activity. I began to understand that doing something and allowing something to happen can both have moral consequences, but people may judge them differently.
Protecting Passengers and My Sense of Responsibility
My results also showed that protecting passengers mattered a lot to me. This was particularly interesting because the Moral Machine made me imagine myself not simply as an outside observer but as someone who might actually be sitting inside the vehicle.
I began to think: If I am the passenger, should the car sacrifice me to save pedestrians? At the same time, I could not ignore the fact that pedestrians are also human beings with lives and families.
This created a conflict between self-preservation and social responsibility. I realised that if autonomous vehicles are expected to make such decisions, society must decide beforehand how much priority should be given to passengers. My own result suggests that I instinctively give considerable importance to protecting those inside the vehicle.
This also made me understand why programming morality into AI is so complicated. A programmer cannot simply write “save the maximum number of people” and consider the ethical problem solved. Questions about passengers, pedestrians, intervention, legality, and responsibility immediately complicate that principle.
Upholding the Law
Another strong feature of my result was that upholding the law mattered a lot to me. This preference became particularly meaningful in scenarios where pedestrians were crossing against a red signal.
When I encountered such situations, I found myself considering whether the person's own behaviour should affect the decision. If someone is following the law, I naturally feel that the person deserves protection. If someone is deliberately violating a traffic signal, I find the moral situation different.
However, the activity also made me question this position. Does breaking a traffic rule mean that a person's life becomes less valuable? My answer is no. The law can influence responsibility, but it should not automatically determine the value of a human life.
Therefore, the activity helped me distinguish between legal responsibility and human worth. A person may be legally responsible for risky behaviour, but that does not mean that the person has less intrinsic value as a human being.
Saving More Lives Versus Valuing Individual Lives
The strongest moral principle visible in my results was my preference for saving more lives. This initially seemed completely logical to me. If I have two choices and one results in one death while the other results in several deaths, I would naturally choose the option that saves more people.
But the activity made me realise that this principle can become disturbing when individual identities are introduced.
For example, if one side contains a professional or a pregnant woman and the other side contains several people, should I calculate their lives numerically? If one side contains a criminal and another contains an innocent person, should the criminal automatically be sacrificed?
I realised that human life cannot be reduced entirely to mathematics. Numbers matter, but circumstances, dignity, responsibility, equality, and individual rights also matter.
This became one of my most important learning outcomes: ethical decision-making requires both consequences and principles.
My Attitude Towards Autonomous Vehicles
The questionnaire after the scenarios revealed another important aspect of my thinking. I indicated that I believed my Moral Machine decisions could be used to program self-driving cars to a very great extent. At the same time, my willingness to buy a self-driving car and my trust in future machines were closer to the middle of the scales. Most strikingly, I expressed a very high level of fear that machines could become out of control.
This combination is quite revealing for me.
I am willing to participate in discussions about how machines should make moral decisions, but I am not completely comfortable handing those decisions over to machines. I can see the usefulness of autonomous technology, but I also recognise its potential dangers.
I therefore find myself in a position of conditional trust. I do not reject AI or autonomous vehicles, but I believe they require strong ethical frameworks, human oversight, transparency, and accountability.
My Major Learning Outcomes
Through this activity, I learned several important things about myself and about artificial intelligence.
1. I learned that morality is complicated.
Before doing the activity, I believed that moral decisions could usually be solved through common sense. The Moral Machine showed me that when two harmful outcomes are unavoidable, there may be no completely satisfactory answer.
2. I became aware of my own moral biases.
The result showing the cat as my most saved character and the man as my most killed character made me examine patterns in my own choices that I had not consciously recognised.
3. I learned that numbers strongly influence my ethical thinking.
My strong preference for saving more lives shows that I naturally consider consequences when making moral decisions.
4. I learned that law influences my moral judgement.
My strong preference for upholding the law showed me that I tend to consider whether people have followed established rules when evaluating difficult situations.
5. I learned that I value non-intervention.
My response indicated that avoiding intervention mattered a lot to me, which made me think more deeply about the difference between actively causing an outcome and allowing an existing course of events to continue.
6. I learned that I give significant importance to passenger safety.
My strong preference for protecting passengers made me realise that I consider responsibility toward people inside an autonomous vehicle to be particularly important.
7. I learned that AI ethics is also about human ethics.
The activity made me realise that the problem is not simply “Can a machine make a decision?” The deeper question is “Whose values should the machine follow?”
8. I developed greater critical self-awareness.
The activity forced me to examine not only what I chose but why I chose it. This is perhaps the most valuable learning outcome for me because it transformed the activity from a game into an exercise in self-reflection.
Reflection as an English Literature Student
As an English literature student, I found the activity particularly meaningful because literature frequently places characters in situations of moral conflict. Tragedy, for example, often emerges when characters must choose between competing values, and every decision carries consequences.
The Moral Machine gave me a similar experience in a digital environment. Instead of analysing the ethical decisions of a fictional character, I became the decision-maker myself.
This changed my perspective. When I read about a character making a morally questionable decision, I can sometimes judge that character from a distance. In the Moral Machine, however, I was forced to make the decision myself. This made me realise how easy it is to judge others when we are not personally responsible for the consequences.
The activity therefore connected my literary understanding of ethics, human nature, responsibility, conflict, and judgement with contemporary debates surrounding artificial intelligence.
Conclusion
Overall, the Moral Machine Activity was not simply an activity about self-driving cars for me. It became an exercise in understanding my own moral framework.
My results showed that I strongly value saving more lives, protecting passengers, upholding the law, and avoiding intervention, while my preferences concerning gender, age, fitness, and social value were comparatively moderate. The final result, in which the cat appeared as my most saved character and the man as my most killed character, further encouraged me to question the unconscious patterns behind my decisions.
The activity ultimately made me understand that AI does not create morality; humans have to give it moral rules. Therefore, if we want machines to make decisions affecting human lives, we must first examine our own values, biases, assumptions, and definitions of justice.
My most important takeaway is that I should not simply ask, “What should the machine do?” I should also ask, “Why do I believe that this is the right thing to do, and what does my choice reveal about the values I hold?”
For me, that is the real learning outcome of the Moral Machine Activity: the machine was not only testing how I would programme a car; it was, in a way, making me examine myself.
A Pedagogical Shift from Text to Hypertext | Language & Literature to the Digital Natives
A Pedagogical Shift from Text to Hypertext | Language & Literature to the Digital Natives
Summary and Critical Reflection on Prof. Dilip Barad's FDP Session
Prof. Dilip Barad's session, delivered under an International Faculty Development Programme, engages one of the more provocative implications of postmodern literary theory: that the very structures which once destabilised the "author" and the "text" can now be traced, with equal force, in the classroom. The talk takes as its point of departure Silvio Gaggi's study From Text to Hypertext: Decentering the Subject in Fiction, Film, the Visual Arts, and Electronic Media, and uses it as a conceptual bridge between literary theory and pedagogy, a move that is, in itself, worth pausing on. Literary theory is rarely permitted to leave the seminar room and enter the lecture hall as a description of the lecture hall itself. Barad's session insists that it must.
Gaggi's Premise and Its Postmodern Lineage
Gaggi's argument rests on a foundational tenet of postmodern writing: that the subject, understood here as the stable, coherent, authorial self, is unstable, fragmented, and decentered. This is not a new claim in isolation. It echoes Roland Barthes's "Death of the Author" and Michel Foucault's "What Is an Author," both of which dismantle the Romantic figure of the author as the sovereign origin of meaning. What Gaggi contributes is a technological extension of this argument. Where Barthes and Foucault were responding to structuralist and poststructuralist reading practices, Gaggi observes that electronic media, and specifically the hypertextual and networked structures enabled by computers, do not merely theorise the decentered subject; they materially produce it. A hypertext narrative has no single, fixed sequence. A reader constructs a path through nodes and links, and in doing so becomes a co-author of the text's unfolding, dispersing authorship across a network of choices that no longer belongs to any one writing consciousness. Gaggi pushes this further still by noting that literature generated and circulated on computer networks exceeds even the physical and proprietary limitations of something like a CD-ROM, so that the notion of a bounded, individually authored work becomes, for all practical purposes, unsustainable.
The Pedagogical Transposition
The intellectual core of Barad's session lies in the analogical leap he makes from this theory of textuality to a theory of teaching. If the postmodern subject in fiction, film, and electronic media is decentered, he asks, what happens when we substitute the literary "subject" with the pedagogical one? Barad identifies this pedagogical subject as a triad: the Core Content that is taught, the Teacher who transmits it, and the Taught, that is, the learner who receives it. Traditionally, all three occupy fixed and hierarchical positions. Content is stable and canonical. The teacher is the centred authority who mediates access to that content. The learner is positioned as a receiver, moving along a single, teacher-determined path toward understanding.
Barad sir's argument is that digital pedagogy dismantles this arrangement in precisely the way hypertext dismantles the literary subject. Once content exists in networked, hyperlinked, multimodal forms, accessible through search, recommendation algorithms, peer-generated commentary, and non-linear digital pathways, it can no longer be treated as a fixed body transmitted from a single centred source. The learner today, the so-called digital native, does not receive content in the way earlier generations did. They arrive already accustomed to navigating decentred information environments, moving associatively rather than linearly, and constructing their own paths of meaning much as a hypertext reader does. Barad's most striking formulation is that this decentering of the learner also entails a decentering of what he calls "Teachership," the very notion of the teacher as sole authority and origin of knowledge. If the reader-become-coauthor displaces the singular literary author, then the digitally native learner, by analogy, displaces the singular pedagogical authority of the teacher.
Significance for Postgraduate Literary Study
For a postgraduate student of English literature preparing for research-level engagement with literary theory, this session performs a useful and somewhat rare function. It demonstrates theory in active application, rather than as an inert body of concepts to be memorised for an examination. Poststructuralist and postmodern ideas about decentred subjectivity are often taught in the abstract, tethered to canonical essays and literary case studies. Barad's move to apply this same theoretical apparatus reflexively, to the classroom in which the theory is itself being taught, models exactly the kind of interdisciplinary and self-aware critical thinking that research in the humanities increasingly demands. It also anticipates debates that have only intensified since 2021 around digital pedagogy, learner autonomy, and the destabilising effect of networked information environments on traditional educational hierarchies, debates that have gained further urgency with the proliferation of AI-assisted and algorithmically mediated learning.
A Note of Critical Caution
It is worth registering one productive tension in the argument, which a postgraduate reader should not overlook. An analogy is not an identity. The claim that pedagogical structures decenter in a manner homologous to literary structures is persuasive as a metaphor, but it elides certain disanalogies. A hypertext reader's decentering is largely an aesthetic and interpretive event; a learner's decentering carries epistemic and institutional consequences, since assessment, credentialing, and disciplinary knowledge still depend on some functioning notion of authoritative content and evaluative expertise. Barad's talk, in gesturing toward the loss of "Teachership," raises a genuinely important question without fully resolving it: if both content and teacher are decentered, what structure of accountability or rigour remains to anchor learning at all? This is not a flaw in the argument so much as an invitation, one that a postgraduate researcher might productively take up in further work on digital humanities and pedagogy.
Conclusion
Taken as a whole, the session offers a compact but conceptually rich argument: postmodern theories of textual decentering, first developed to describe fiction, film, and electronic media, can be read as diagnostic of contemporary pedagogy itself. In doing so, Barad reframes the digital native not merely as a student who uses new tools, but as a subject formed by, and formative of, a genuinely post-textual mode of knowing, one in which content, teacher, and learner are no longer arranged along a single stable axis of authority.
BREAKING THE BOARD
How Dilip Barad Rewired the Literature Classroom for the Digital Native
A close reading of the FDP “A Pedagogical Shift from Text to Hypertext” - Part 1
There is a moment in this presentation where Dilip Barad stops talking about technology and starts talking about ontology what a self even is once it goes online. This is not a slide deck about Zoom links dressed up as pedagogy; it is a claim that the medium has rewritten the mind, and that literature teachers who ignore this are teaching in a language their students no longer speak.
I. Hypertext Is a Philosophy, Not a Format
Barad's dry technical definition of hypertext - “direct links to related text, images, sound” over HTML/HTTP hides its real argument in the verbs he highlights: storing, linking, interacting, transmitting. A printed poem sits still on the page; hypertext is built to send the reader elsewhere. That architectural fact is the seed of everything that follows.
He then imports Silvio Gaggi's From Text to Hypertext, which argues that in postmodern and digital media, the author dissolves because readers become co-authors:
“The subject the self is unstable, fragmented, and decentered... the notion of individual authorship may for all practical purposes be lost.”
Theory didn't just describe this instability the network built the machine that enacts it.
II. If the Author Dissolves, So Can the Teacher
Barad's sharpest move: he substitutes teacher for author in Gaggi's own sentence.
“Subject = Core Content, the teacher, and the taught. This decentering of learners and the notion of teachership may for all practical purposes be lost.”
This is argument by analogy, not paraphrase: once content, learner, and instructor are all networked, no single node — not even the teacher controls meaning anymore. His answer is not despair but three named models: Blended Learning, Flipped Classroom, and Mixed Mode each a different answer to who organises the classroom once the centre cannot hold.
III. The Salad Bowl, Not a Melting Pot
Barad's model keeps every ingredient distinct rather than dissolving it into one mixture:
IV. Theory Leaves the Slide: The Toolkit in Action
1 - Glassboard / Lightboard
Writing on transparent glass while facing the camera restores the one thing video calls destroy: eye contact while teaching. Applied to the three formats of business letters, it lets Barad draw and compare layouts side by side something a bullet-point slide cannot do.
▶ Glassboard / Lightboard demos
Studio setup and its first trial.
▶ Letter Writing: Three Formats
Traditional vs. American vs. Hanging-Paragraph, drawn live.
https://youtu.be/Z9SC-hTkO10?si=CShzKt3G23dyrIiY
2 - Flipped Learning: “In Search of Questions”
Not “watch the lecture, do the exercise” the classroom's job becomes generating questions, consistent with the teacher no longer being the sole centre of content.
▶ Flipped Learning: In Search of Questions
Barad's own framing of what flipped learning is for.
https://youtu.be/hWDCS38kxFc?si=7DM8OGrEQYZS7vk5
3 - OBS + Lightboard: Making the Abstract Physical
For Literature and Science, Barad sketches literal axes converging on Reason, turning an abstract argument into a geometric shape. For Simon Armitage's lockdown poem, handwriting overlaid on moving imagery reaches back to Kalidasa's Meghadūta making a 16-centuries-old intertextual echo visible rather than footnoted.
▶ Literature and Science
Earth-centred cosmology mapped against the modern “Infodemic.”
https://youtu.be/5rrvYQ5aFEQ?si=8_kPp9eVgHD34oZe
▶ Lockdown: Pictorial Journey of a Pandemic Poem
Armitage's poem overlaid on imagery reaching back to Kalidasa's cloud-messenger.
https://youtu.be/QacfiCC-m8Q?si=IBQuEcZZDrgHamEr
4 - Deconstructive Reading of Sonnet 18
Barad circles and cancels the sonnet's binaries Beloved/Nature, Summer/Eternal live on the glassboard, turning deconstruction from description into performance. This becomes a full TED-Ed lesson (Watch → Think → Dig Deeper → Discuss), fusing flipped learning with deconstruction: students, not Barad, now decenter the text.
▶ Deconstructive Reading of Sonnet 18
Live glassboard reading, later a full TED-Ed interactive lesson.
https://youtu.be/ohY-w4cMhRM?si=0jTFu6n5tgpBVnnl
5 - The Loop Closes: Students Question Barad
A live hybrid Q&A on Derrida and Deconstruction, where learners hold the microphone. The teacher who admitted his own centrality “may for all practical purposes be lost” appears here as one tile among many the decentered node Section II predicted.
▶ Flipped Classroom Q&A: Derrida and Deconstruction
Mixed-mode session students questioning, not being lectured at.
V. What Part 1 Proves
Hypertext did not just change where literature is taught it changed who gets to be at the centre of the room. Gaggi's decentered author, imported into the classroom, becomes a decentered teacher. Barad's five tools don't mourn that shift; they are five different designs for teaching through it, so literature's essence survives the very medium dismantling the old hierarchy around it.
Cracking the Code: Language, Literature and the Hypertext Classroom
A Close Reading of Part 2 of Dr. Dilip Barad's FDP
“A Pedagogical Shift from Text to Hypertext: Language and Literature to the Digital Natives”
Introduction: The Sequel That Solves the Problem
If Part 1 of Dr. Dilip Barad's Faculty Development Programme asked a hard question, teachers what happens to literature when it leaves the printed page, then Part 2 supplies the working answers. This is not theory for its own sake. It is a toolkit built from a real problem. Two things get lost the moment a literature or language classroom moves online: the sound of the human voice and the shared cultural ground that literature quietly depends on. Barad tackles both, first through the presentation slides, and then through two demonstration videos that show, tool by tool, how the digital classroom can be rebuilt so nothing is lost.
The Vanishing Voice: Solving the Pronunciation Problem
Barad opens with a problem every language teacher recognises the moment a class goes online. Pronunciation, stress and modulation are the load bearing walls of spoken language, yet a student listening through a laggy microphone or a muffled speaker may simply lose the word. The slide is blunt about it: students may find it difficult to understand the right word pronounced by the teacher, and the essence of linguistic units gets lost in transmission.
His answer is not a workaround, it is a redesign. He turns to Live Caption in Chrome, which subtitles any audio playing in the browser in real time, to Tactiq and Meet Transcript, which capture an entire class session as searchable text, and to Voice Typing in Google Docs, which converts speech into a live transcript as the teacher talks. Together these tools do something a whiteboard never could: they give sound a visible, permanent, searchable body. The ear is no longer the only route into the word.
"Students may find it difficult to understand the right word pronounced by the teacher, and the essence of linguistic units."
-Slide, Hypertext Pedagogical Shift, Part 2
Teaching a Foreign Literature: Naming the Real Obstacles
The second half of the presentation turns from language to literature, and Barad asks the harder question first. Can a teacher deliver the very quintessence of a poem, a play, a novel, in an online classroom? Can students genuinely adore literature written in a language and culture not their own?
What makes this section powerful is that Barad refuses to leave the difficulty vague. He names seven precise obstacles that any Indian classroom teaching English literature runs into:
• Cultural anonymity, the classroom does not share the writer's cultural reflexes
• The social code of conduct embedded in the text
• Religious inconspicuousness, allusions that assume a faith the reader may not hold
• Mythical aloofness, references to myths outside the student's inherited canon
• A difference in shared collective unconsciousness, Jung's term for the buried cultural memory a text leans on
• Geographical remoteness from the setting of the text
• Historical distance from the moment the text was written
This is a genuinely useful checklist for any student of literature, because it converts a vague feeling of foreignness into seven specific, teachable gaps, each of which hypertext can help close.
Deconstructing a Poem in Real Time: Hawthorns, Milk and a Blue Pitcher
Barad then proves the checklist works by demonstrating it live, on a genuinely difficult poem. The lines in question read:
"While below the Hawthorns smile like milk splashed down
From Noon's blue pitcher over mead and hill"
For a student with no access to the English countryside, this image is close to unreadable. What does a hawthorn look like from above? Who is Noon, and why does she own a blue pitcher? Barad's method is a small masterclass in hypertextual close reading. A Google Image Search for hawthorn shrubs in bloom shows exactly what the poet saw, white blossom scattered across green fields like spilled milk, seen from a cloud's height. Then, in the most striking move of the whole presentation, he identifies Noon's blue pitcher not as a generic image but as a direct reference to Susan Noon's painting Blue Pitcher With Flowers, discovered, as he candidly admits, thanks to Google Image Search.
[ Hawthorn shrubs in bloom, resembling splashed milk from above ]
[ Susan Noon, Blue Pitcher With Flowers ]
This single example does more to justify hypertext pedagogy than any amount of theory could. A metaphor that would have stayed opaque on a printed page becomes visible, traceable, and memorable the moment it is linked outward to an image and a painting. The poem is not simplified, it is illuminated.
Google Arts and Culture: Hypertext as Decentering
Barad names Google Arts and Culture as a genuinely valuable resource, and builds a worked lesson plan around it: teaching the myth of Icarus and Daedalus through a webquest called Fall of Icarus. A search on the platform returns seventy three connected items, paintings, spotlight essays, video interpretations, all clustered around one myth.
The pedagogical point here is sharper than it first appears. The learning outcome is not simply knowing the myth, it is grasping the theoretical concept of decentering the centre, a deconstructive reading practice. A student who moves through Bruegel's painting, then a set of poems about the myth, then a video reconstruction, is enacting Derrida's idea in the very act of navigation. There is no single authoritative version of the Icarus story anymore, only a web of versions, each one shifting the centre of meaning. Hypertext does not just illustrate deconstruction, in this lesson plan it performs it.
[Google Arts and Culture, Fall of Icarus webquest, 73 connected items ]
Turning the Classroom Into a Live Collaborative Workspace
▶ Using Google Drive for Engaging Learners in Online Remote Teaching, Part II, Dilip Barad
https://youtu.be/MFTzlocEb04?si=8YhoJshDSsDrDNOF
Where the slides argue the case, this video shows the hands actually at work. Barad opens a single Google Doc to a live audience and asks everyone to write an imaginary dialogue based on one picture, simultaneously. The result, dozens of cursors moving across the same page at once, is a striking visual proof of what peer learning looks like in practice. Students are not submitting work into a void, they are watching each other write, borrow, and revise in real time.
Three moves stand out as genuinely clever pedagogy. First, he lets Google Docs' built in grammar and spelling suggestions do quiet, constant correction, so the machine handles small errors while the teacher's attention stays on ideas. Second, he uses Google Sheets to drill sentence transformation, active voice into passive voice, arguing that these structures must be hardwired into the mind because the grammar of writing is simply not the grammar of speech. Third, he brings out Google Jamboard as a genuine digital blackboard, sticky notes, freehand drawing and mind maps built collaboratively, which restores the physical, communal energy of a real classroom board.
The Glass Board and the Problem of the Turned Back
▶ Using Glass-board / Learning Glass for Engaging Learners in Online Remote Teaching, Part I, Dilip Barad
https://youtu.be/JcSozj7dTpo?si=fpPpqgBFOjDUhfmC
Webinar Series, SCOPE, KCG, Gujarat, the mechanics of the Learning Glass
The second video answers a question the earlier slides only gestured at: how does that glowing, transparent board actually work? Barad's diagnosis of the ordinary whiteboard is precise and worth remembering, the moment a teacher turns to write, eye contact with the class is lost, and with it, a measurable amount of engagement.
His solution is a piece of engineering as much as pedagogy. A sheet of glass, framed with LED strip lighting so the ink glows, set against black curtains for contrast, written on with fluorescent neon markers. The teacher writes facing the audience, so the words unfold before the students' eyes instead of appearing behind a turned back. The one real technical obstacle is that text written on the front of the glass reads backwards to the camera, solved by mirroring the video feed horizontally in software such as OBS, or a phone based tool like DroidCam. It is a low cost, almost handmade solution to a problem every online teacher has felt but rarely named.
Reflection: What This Means for a Future Classroom
Taken together, the slides and the two videos sketch a complete method rather than a set of isolated tricks. Language teaching gets solved through captioning and transcription. Literature teaching gets solved through image search, painting, and Google Arts and Culture, each closing one of the seven named gaps between the student and the foreign text. Collaboration gets solved through Docs, Sheets and Jamboard. Presence gets solved through the Learning Glass. Nothing here is decorative technology added for its own sake, every tool answers a specific, named pedagogical loss.
For a student preparing to teach English literature, the lesson is genuinely reassuring. Teaching online does not have to mean teaching diminished. Done with this much intention, hypertext does not water literature down, it opens new doors into the very difficulty that makes literature worth teaching in the first place.
FROM TEXT TO HYPERTEXT
PART 3 - Generative Literature, Digital Humanities, and the Future of Assessment
A close reflective analysis of Dr Dilip Barad's FDP session, "A Pedagogical Shift from Text to Hypertext: Language & Literature to the Digital Natives"
🔗 Access the full Part 3 presentation
https://dilipbarad.com/fdp/hypertext3/index.html
Slide deck referenced throughout this analysis
Where Literature Stops Being Written and Starts Being Generated
If Part 1 of this FDP asked us to accept the digital native and Part 2 asked us to solve the practical problems of teaching them, Part 3 asks something far more radical: what happens to “literature” itself once a machine, not a human being, is the one producing the text? This is not a session about tools anymore. It is a session about ontology — about what counts as a literary work when the author is an algorithm. I walked in expecting more classroom hacks. I walked out questioning the very definition of authorship I had taken for granted through three years of an English degree.
1. Generative Literature: When the Dictionary Becomes the Author
The presentation's central provocation is French theorist Jean-Pierre Balpe's definition of Generative Literature: literary texts produced continuously and endlessly through a specific dictionary, a set of rules, and algorithms. Balpe is not a peripheral figure invoked for decoration he is among the earliest pioneers of computer-generated poetry, having built generative French poetry systems since the 1980s using layered metalanguages. His argument, elaborated in the essay linked directly on the slide, is that because the text is produced by a machine rather than written by a human author, it demands “a very special way of engrammation” and, consequently, a wholly new way of reading one alert to the literary experience of time itself, since a generative text is never fixed, never final, never the same twice.
Generative literature is the production of continuously changing literary texts by means of a dictionary, a set of rules, and/or the use of algorithms.
-Jean-Pierre Balpe, “Principles and Processes of Generative Literature”
This is, in effect, a slow-motion demolition of the Romantic idea of the author as sole originator of meaning. Balpe himself reframes the human role not as author but as “meta-author” the designer of a system that then goes on to write. The poem is no longer a fixed artefact; it is one “temporary specimen of an infinite family of virtual texts.” For a literature student trained to hunt for authorial intention, this is genuinely destabilising.
Can a Computer Write Poetry? Kirschenbaum vs Tarantino
The slides stage a deliberate confrontation. On one side stands Matthew Kirschenbaum, the digital humanities scholar whose landmark essay “What Is Digital Humanities and What's It Doing in English Departments?” legitimised computational method inside literary study. On the other stands Quentin Tarantino's blunt, almost defiant assertion: “You can't write poetry on the computer.” The juxtaposition is the argument. Barad is not resolving the tension he is forcing us to sit inside it, because that tension is exactly what a digital-native classroom has to negotiate every single day.
The presentation resolves this tension experientially rather than theoretically, through the “Bot or Not” game (botpoet.com), where visitors read a poem in the slide's example, “May Night” and guess whether it was written by a human or a computer. This is close reading weaponised as a literacy test for the algorithmic age: can you still tell? The slide also displays a Google Form for classroom voting, turning the theoretical question into a live, gradeable activity. Alongside it, the Poem Generator (poem-generator.org.uk) is shown producing sonnets, haikus, villanelles, and even acrostics on demand a small, slightly unsettling factory of literary form.
2. From Close Reading to Macroanalysis: Jockers, Culturomics, and Big Data
If generative literature asks what happens when computers write texts, this section asks what happens when computers read them at a scale no human ever could. Matthew Jockers's Macroanalysis: Digital Methods and Literary History proposes exactly the shift its subtitle promises: from the close reading of a single novel to the “macro” reading of thousands of novels simultaneously, hunting for patterns invisible at the level of the individual sentence.
The slides pair this with Erez Lieberman-Aiden and Jean-Baptiste Michel's Culturomics the Harvard/Google project behind the famous Ngram Viewer, quantifying culture itself by tracking word frequencies across millions of digitised books. Their talk, titled “Uncharted: Big Data as a Lens on Human Culture,” makes an audacious claim: that shifts in a civilisation's values, obsessions, and blind spots can be read off a graph of word usage over centuries. It is distant reading taken to its most literal, most quantitative extreme.
Close reading asks what a text means; macroanalysis and culturomics ask what a million texts are doing without any single reader noticing.
3. Corpus Linguistics in Context: The CLiC Dickens Project
This was, without question, the section that hit hardest as a language student. CLiC (Corpus Linguistics in Context) is a real, working web application built collaboratively by the Universities of Birmingham and Nottingham, led by Professor Michaela Mahlberg. It began life in 2013 specifically to study Charles Dickens through corpus stylistics the marriage of statistical, computer-assisted text analysis with literary-critical interpretation.
What makes CLiC extraordinary is its stated purpose: not merely to count words, but to show how computer-assisted methods can “lead to new insights into how readers perceive fictional characters.” This reframes corpus linguistics as a tool of characterisation study you can, for instance, run every instance of how a Dickensian character's body language is described across an entire novel, and watch a pattern of authorial technique emerge that no single close reading would ever surface, because no human reader holds four and a half million words in working memory at once.
The mechanism underneath nearly all of this is KWIC - Key Word In Context the standard format for concordance lines, where every occurrence of a chosen word is displayed with a fixed span of surrounding text so the reader can scan usage patterns at a glance. The slides correctly credit the term to Hans Peter Luhn, but the fuller story deepens the point: Luhn, a German-American IBM researcher, unveiled his automatic KWIC indexing system at the International Conference for Scientific Information in Washington, DC, in November 1958, building on a manual indexing method the librarian Andrea Crestadoro had proposed for the Manchester libraries as early as 1856–64. In other words, the concordance one of literary scholarship's oldest tools for tracing a word's every appearance in a text was mechanised nearly seventy years before CLiC, and Luhn's automation of it is a direct ancestor of every “Ctrl+F” a student now takes for granted.
KWIC is the most common format for concordance lines. The term was first coined by Hans Peter Luhn a demonstration nineteenth-century librarians could never have imagined, built to make Dickens's four-million-word world searchable in seconds.
4. Reimagining Assessment: The Digital Portfolio
Having dismantled what literature is and how it can be read, the presentation turns, with real pedagogical courage, to how learning itself should be judged. Its answer is the Digital Portfolio: every piece of student work hyperlinked onto a personal website, so that the process of learning not just its final grade becomes visible, cumulative, and permanent. The presentation anchors this with a real, working example: a Google Sites blended-learning portfolio built for exactly this purpose, still standing today as a model of what a fully hyperlinked student archive can look like.
It is imperative that students be able to curate, archive, and expand on the work they are producing in class. Education must help students internalize the core subject as well as vital digital literacy skills such as creating their own digital web presence.
- Holly Clark, educator
Clark's framing matters because it refuses to treat digital literacy as a soft add-on to “real” subject learning. She insists the two must be internalised together a student who cannot curate and share their own intellectual work digitally is, in her framing, incompletely educated for the world they are entering. A portfolio, unlike a single sealed exam script, is a living record: it can be expanded, revised, and cited by the very student who built it, years later.
Synthesis: What Part 3 Actually Argues
● Generative literature (Balpe) forces literary studies to separate “author” from “text-producer” for the first time in the discipline's history.
● The Kirschenbaum–Tarantino tension is not a debate to be won; it is the permanent condition of teaching literature to digital natives.
● Macroanalysis and Culturomics (Jockers, Aiden & Michel) scale close reading up from one text to millions, trading depth for pattern.
● CLiC Dickens shows this scaling can still serve close, character-level literary insight distant and close reading are not enemies.
● KWIC / concordancing (Luhn, 1958) is the quiet, decades-old technology underneath almost every “digital” reading tool students use today.
● Assessment itself must evolve: a hyperlinked Digital Portfolio replaces the single exam script with a living, citable archive of learning.
My Takeaway
Part 3 leaves me with an uncomfortable but exciting question I did not have before this FDP: if a machine can generate a technically competent sonnet, and a corpus tool can surface a pattern in Dickens that no human reader ever consciously noticed, what exactly is the irreducible work of the literary scholar? My answer, provisionally, is interpretation under uncertainty the willingness to say what a pattern means, not just that it exists. Algorithms can find the pattern. They cannot yet tell us why it matters. That, for now, remains ours to do and it is precisely the skill this entire FDP, across all three parts, has been quietly training us to sharpen for the hypertext age.
Here is Presentation upon the Blog for better understanding:
Refrences:
Barad, Dilip. "A Pedagogical Shift from Text to Hypertext | Language & Literature to the Digital Natives." YouTube, uploaded by DoE-MKBU, 15 Sept. 2021,
https://www.youtube.com/watch?v=c1H-ejKTGQM
Barad, Dilip. "A Pedagogical Shift from Text to Hypertext: Part 1." DilipBarad.com,
https://dilipbarad.com/fdp/hypertext1/index.html
Barad, Dilip. "A Pedagogical Shift from Text to Hypertext: Part 2." DilipBarad.com,
https://dilipbarad.com/fdp/hypertext2/index.html
Barad, Dilip. "A Pedagogical Shift from Text to Hypertext: Part 3." DilipBarad.com,
https://dilipbarad.com/fdp/hypertext3/index.html
MIT Media Lab. "Moral Machine." Moral Machine, Massachusetts Institute of Technology,
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