# Introduction | Digital Communication
The term digital finds its linguistic roots in the Latin word digit or digitus, which translates directly to 'fingers' In contemporary scholarly contexts. digital communication refers to the transmission of data, messages, or information through electronic mediums and digital devices.In modern professional and academic environments, digital communication fulfills several vital operational functions. It enables seamless collaboration, allowing remote teams to share updates, documents, and creative ideas instantly regardless of physical location. Furthermore, it enhances time efficiency by facilitating rapid responses that significantly reduce delays in decision-making. Digital communication also plays a crucial role in relationship building by maintaining continuous connections with colleagues, clients, and peers, while simultaneously shaping an individual's personal branding and professional identity through their online communication style and etiquette. It offers global connectivity, permitting individuals to interact across traditional geographical boundaries without physical constraints.
Encoding: Since the subject matter of communication is theoretical and intangible, its further passing requires use of certain symbols such as words, actions or pictures etc. Conversion of subject matter into these symbols is the process of encoding.
Receiver: The person who receives the message or for whom the message is meant for. It is the receiver who tries to understand the message in the best possible manner in achieving the desired objectives.
Feedback: It the process of ensuring that the receiver has received the message and understood in the same sense as sender meant it.
Digital communication exhibits distinct architectural traits that differentiate it from traditional print and face-to-face interactions. It is intrinsically instant and global, enabling rapid interaction across international borders at any time. It provides users with immense flexibility regarding when, where, and with whom they choose to communicate. A primary challenge of text-based digital media is its limited non-verbal cues, as the absence of facial expressions, body gestures, and vocal tone can frequently lead to misinterpretation. To compensate, digital media relies heavily on multimodal communication, combining text, images, emojis, audio clips, and video elements to construct meaning. And digital messages are persistent and replicable, meaning they can be easily stored, copied, modified, forwarded, and redistributed, while offering a cost-effective and convenient means of networked social interaction.
Engaging responsibly in digital discourse requires strict adherence to established standards of digital etiquette or netiquette. A foundational rule of online netiquette is avoiding the use of all capital letters, as writing in ALL CAPS is universally interpreted in digital spaces as shouting. Communicators are expected to practice courtesy and respect, mute their microphones when not speaking during video meetings, refrain from interrupting others, and handle differing opinions constructively. Users must exercise digital responsibility by verifying information prior to forwarding or posting, ensuring they do not spread fake news, spam, or offensive remarks.
- Be respectful.
- Avoid offensive comments.
- Don't spam.
- Avoid forwarding fake news.
- Don't interrupt during video meetings.
- Mute yourself when not speaking.
- Respect different opinions.
- Think before posting.
- Avoid ALL CAPS
- No rude language
- Refrain from sarcastic remarks
- Avoid using offensive emojis
A critical review (also known as a summary and critique) evaluates a single academic article. Unlike a literature review that synthesizes multiple sources, a critical review combines a concise summary of one paper with a thoughtful evaluation of its arguments, methodology, and conclusions.
# Preparation & Analytical Reading
Read the article quickly to get the "gist" and grasp its main overall direction
Step 3: Detail reading and Note taking
Re-read the article carefully, taking detailed notes on the subject, core conclusions, supporting arguments, and evidence. Compare your notes with a peer to verify main points
Construct a visual flow diagram or argument map showing how the main points of each paragraph logically relate to one another
Set the article aside to reflect and form a clear personal stance on what you have read
Review your notes and argument map to identify specific strengths and weaknesses. Frame your critique around these key criteria
- Theoretical Claims: Are the concepts logical and convincing?
- Evidence & Methodology: Is the research procedure, sample size, or population appropriate?
- Statistical Support: Are the data and proofs adequate to support the conclusions?
- Unstated Assumptions: Does the argument depend on unstated or problematic premises?
- Merits & Applications: What ideas do you agree with, and how can they be applied to new contexts
First-person perspective: Use “I” to express your critical position.
Example: I argue that the author's conclusion requires further evidence.
Present simple tense: Use present tense to discuss an author's arguments.
Example: The author argues that technology influences human relationships.
Referencing convention: Use scholars' surnames in the text and arrange references alphabetically by surname.
Example: Ryckmans argues that language shapes cultural identity.
Use direct quotations only when the author's exact words are important to your analysis. Otherwise, paraphrase the ideas in your own words while preserving their original meaning. Always cite both quotations and paraphrased ideas.
Direct quotation: The author describes technology as “a transformative force.”
Paraphrasing: The author presents technology as a major influence on social change.
Once you finish your first draft, ensure that your arguments are sufficiently developed and supported by evidence. Avoid vague statements and simple summaries. Every paragraph should contribute to your central argument.
Examples:
Weak: The article is very good
Strong: The article offers a convincing argument, supported by relevant textual evidence.
Revision improves the organization and clarity of your arguments, while proofreading corrects grammatical, spelling, and punctuation errors. If possible, leave your draft aside for a day or two before reviewing it with fresh eyes.
Examples
Wordy: The author makes an argument that is very important and significant.
Concise: The author's argument is significant.
Incorrect tense: The author argued that technology influences society. (when discussing the article's ongoing argument)
Preferred: The author argues that technology influences society.
- Is my central argument clear?
- Does each paragraph contain evidence and critical evaluation?
- Have I used academic language and consistent tenses?
- Have I avoided unnecessary quotations and repetition?
- Have I checked grammar, spelling, citations, and references?
Artificial Intelligence (AI) has changed the way we interact with computers. Earlier, people generally needed technical knowledge or specific commands to perform tasks using computers. Today, tools such as ChatGPT allow us to communicate with computers using ordinary language. We can ask questions, analyse literary texts, summarisze articles, generate ideas, improve our writing and explore complex theories simply by typing instructions.
However, the quality of the response often depends on how we communicate our requirements. A vague question may produce a general answer, whereas a clear and specific instruction can produce a more relevant response. Prompt engineering is the skill of designing and refining instructions to guide AI towards a useful output.
For English Literature students, this skill is particularly useful because AI can assist with brainstorming interpretations, identifying literary devices, explaining critical theories, comparing characters, and improving academic writing. However, AI should be treated as an assistant rather than an unquestionable authority. Students must evaluate its responses, verify facts, and develop their own critical interpretations. We should emphasize on clear instructions, examples, context, and experimentation as important aspects of effective prompting.
# Key Concepts and Terminology
A prompt is the input or instruction we give to an AI system to make it perform a task or generate a response. It may be a question, command, statement, or a combination of these. A prompt can be short or detailed, depending on the task.
Examples:
- “Who wrote Pride and Prejudice?”
- “Explain the concept of the absurd in Albert Camus's philosophy.”
- “Analyse the use of symbolism in The Great Gatsby in 300 words.”
Prompt engineering is the process of creating, testing, and improving prompts to obtain more relevant, clear, and useful responses from an AI model. It involves specifying the task, providing sufficient context, selecting the appropriate tone and format and refining the instruction when necessary.
For example, instead of asking “Analyse Hamlet,” you could write:
“Analyse Hamlet's hesitation in Shakespeare's Hamlet. Discuss his internal conflict and use two relevant moments from the play as evidence. Write in simple academic language for undergraduate students.”
The second prompt guides the AI towards a more focused response.
Model and Large Language Models (LLMs)
A model is the AI system that processes the input and generates an output. A Large Language Model (LLM) is a type of AI model trained on large amounts of text to recognize language patterns and generate human-like text.
ChatGPT uses an underlying AI model to generate responses. Although an LLM can explain ideas, summarise texts, and produce convincing arguments, it does not understand or verify every statement in the same way a human scholar does. Its responses are generated through learned patterns and computational predictions.
Think of it this way:
Prompt: The question or instruction you provide.
Model: The AI system processing your instruction.
Output: The response generated by the model.
Example:
Prompt: “Explain dramatic irony with an example.”
Model: The AI system processes the request.
Output: An explanation of dramatic irony, followed by an example from a play.
# Prompting strategies: Zero-shot, One-shot, and Few-shot
Prompting strategies describe how we instruct an AI model and whether we provide examples to guide its response.
1. Zero-shot prompting: In zero-shot prompting, you ask the AI to perform a task without providing any examples of the expected answer. The model relies on its existing training and the instruction you provide.
Example: “Identify the tone of the following sentence: ‘What a wonderful disaster this has been!’ Answer in one word.”
The AI may respond: Sarcastic.
2. One-shot prompting: In one-shot prompting, you provide one example to show the AI how to perform the task or format its answer.
Example: “Identify the literary device used in each sentence.
Example: ‘The wind whispered through the trees.’ — Personification.
Now identify the device: ‘The stars danced in the sky.’”
The AI can follow the pattern established by your example.
3. Few-shot prompting: In few-shot prompting, you provide two or more examples to help the AI recognize a pattern and apply it to a new task.
Example: “Identify the literary device in each sentence.
‘Life is a journey.’ (Metaphor)
‘The moon smiled at me.’ (Personification)
‘She sells seashells by the seashore.’ (Alliteration)
Now identify the device: ‘The classroom was a zoo.’
Expected answer: Metaphor.
Few-shot prompting is particularly useful when you want the AI to follow a specific format, classification system, or style of analysis. However, providing examples does not guarantee a correct answer.
# Tokens
Tokens are the small units of text that an LLM processes. A token may represent a whole word, part of a word, punctuation, or another text element. Tokens are not exactly the same as words, and the number of tokens in a sentence depends on how the model divides the text. For example, the sentence “I love reading novels!” is divided into smaller text units by the model. The exact division depends on its tokenizer.
Why do tokens matter?
Models have limits on how much text they can process at once. Very long prompts and documents may exceed those limits. Token counts can affect processing time and, in some applications, cost. For literature students, this means that when analysing a long novel or research article, it may be more effective to provide manageable sections and analyse them systematically rather than assume the AI can process an entire text in one go.
# Hallucination in AI
An AI hallucination occurs when an AI generates information that sounds convincing but is false, unsupported, or fabricated. The response may appear academically reliable even when its facts, quotations, publication details, or references are incorrect. For example, a student might ask ChatGPT to provide a quotation from Shakespeare's Hamlet or work citation list. The AI could produce a quotation that sounds Shakespearean but does not actually appear in the play. Hallucinations are especially problematic in academic research because fabricated quotations and references can damage the credibility of your work.
# How can students reduce this risk?
- Verify quotations against the original literary text.
- Check publication details and references in reliable sources.
- Ask the AI to distinguish established facts from interpretations or uncertainty.
- Never assume that a confident response is necessarily correct.
Remember: AI can assist your research, but you are responsible for verifying the information you use.
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