Reflections on Faculty Development Program (FDP) on Research and Writing with AI Assistance

This blog reflects on the One-Week Free Online Faculty Development Program (FDP) on Research and Writing with AI Assistance organized by the Institute of Public Enterprise (IPE), Hyderabad – India’s Premier Business School.

In this blog, I share my key takeaways and insights from the five-day FDP.

Day-1 Idea Generation and Sourcing Literature

Resource person: Dr. Rama Krishna Yelamanchili

In this session, the resource person provided a hands-on demonstration of various AI tools for research and literature review. He practically showcased how these tools can be used to generate research ideas, build literature maps, and manage references. Additionally, he introduced ChatGPT-based tools like Innovative Research Idea Generator, Paper Wizard, Brain Wave, and EduInnovate, which assist in brainstorming research topics and academic writing.

Essential Elements of a Research Paper

• Novel Topic: Choose a unique and relevant research topic.
• Strong Theoretical Framework: Establish a solid foundation with existing theories.
• Extensive Literature Review: Conduct a thorough review to understand research gaps.
• Meticulous Methodology: Ensure precise and well-structured research methods.
• In-depth Analysis: Provide comprehensive and critical data interpretation.
• Robust Results: Present well-supported and credible findings.

Convincing Conclusion: Summarize key insights effectively.

• How to Pick a Research Topic
• Observe real-world events and trends.
• Draw inspiration from your teaching subjects.
• Engage in discussions with peers.
• Explore research journals in your field.
• Conduct an extensive literature review.

AI Tools for Research and Literature Review

Idea Generation:

• PaperGuide.ai
• Editpad
• AppyPie

Literature Mapping & Connections:

• Citrus Search
• ResearchRabbit
• Inciteful
• Litmaps
• Connected Papers

Reference Management:

• Hypothesis Maker
• Zotero
• Bibliography (Zbib)

This session has significantly enhanced my understanding of AI-assisted research and writing. The practical exposure to these tools will streamline my research process, improve idea generation, and enhance the depth and efficiency of my literature review and academic writing.

Day-2 Literature Review with AI Assistance

Resource person: Dr. Kalyani

Conceptual Base of Literature Review

• A literature review is not just a summary but a critical analysis of existing research.
• It connects past studies with your research objectives, identifying trends and gaps.
• A strong conceptual foundation ensures a well-structured and meaningful review.

How to Write a Literature Review

• Begin with a clear objective and research question.
• Identify relevant sources and critically analyze them.
• Organize the review systematically using a structured approach.

Approaches to Writing a Literature Review

• Chronological Approach – Organizes studies based on publication years.
• Reverse Chronological Approach – Begins with the latest research and moves backward.
• Thematic Approach – Groups studies by themes or topics.
• Methodological Approach – Categorizes research based on methods used.
• Conceptual Framework Approach – Focuses on theoretical concepts and models.

Structure of a Literature Review

• Introduction: Defines the research scope, importance, and key themes.
• Body: Uses various approaches (thematic, chronological, etc.) to analyze research.
• Conclusion: Identifies research gaps and highlights future research directions.
• Use of AI in Literature Review AI tools assist in searching, summarizing, and structuring research papers.
• Despite AI’s help, reading a few key papers thoroughly is crucial for understanding theories.
• Demonstrated various AI tools for literature review (as listed in the session).
• New Tool: Lateral.ai – Helps organize and create an LR table for better structuring.

SMART Framework for Literature Review

S – Search: Identify relevant literature using databases and AI tools.

M – Map: Organize and categorize studies based on themes and approaches.

A – Analyze: Critically evaluate research findings, methodologies, and arguments.

R – Refine: Identify gaps, inconsistencies, and missing links.

T – Transform & Rewrite: Synthesize information to align with your research objectives.

Day-3 Research Design with AI tools

Resource person: Dr. Rama Krishna Yelamanchili

The session covered different types of research designs and methods, emphasizing their role in structuring a study. It introduced Mono-method (using a single approach) and Mixed-method research (combining qualitative and quantitative approaches). The book recommended for Mixed-method research was Research Design: Qualitative, Quantitative, and Mixed Methods Approaches by John W. Creswell.

A key takeaway was that methodology is the heart of research, as it ensures rigor and validity in findings. The session also highlighted the use of AI tools like ChatGPT to generate research paper titles and refine research approaches. 

Some AI tools suggested for research support include: 

Prompt Diary – For tracking research-related AI prompts.

CPT Explore - Mentor for Research – A guided research assistant.

Design Academic Guide – For structuring research work.

Research Method Advisor – Providing insights into research methodologies.

Research and Methodology Assistant – Offering assistance in refining research techniques.

Day-4 Data Interpretation and Analysis with AI Tools

Resource person: Dr. Rama Krishna Yelamanchili

1. Scales of Measurement

• Understanding data begins with knowing its measurement scale:Nominal: Categorical data without order (e.g., Gender, Colors).
• Ordinal: Ordered categories but without equal differences (e.g., Rankings, Satisfaction Levels).
• Interval: Numeric data with equal differences but no true zero (e.g., Temperature in Celsius).
• Ratio: Numeric data with a true zero, allowing meaningful ratios (e.g., Weight, Height).

2. Using Statskingdom for Descriptive Analysis Upload data to Statskingdom.com to perform descriptive analysis (mean, median, standard deviation, frequency distribution).

Obtain summary statistics to understand central tendencies and variability.

3. GPT for Data Refinement & InterpretationRevise the output from Statskingdom and re-upload structured data to GPT for further analysis and interpretation.

Utilize GPT-Data Analysis and Report AI to generate insights, trends, and reports.

Explore Advanced Data Analysis in GPT for deeper statistical modeling, correlation, and forecasting.

4. Key TakeawaysStatskingdom helps with fundamental descriptive statistics.

GPT tools enhance interpretation and provide a refined, structured data-driven narrative.

Advanced Data Analysis in GPT assists with deeper insights, trend recognition, and comprehensive reporting.

Day- 5 Finalizing the manuscript with AI Assistance

Resource person: Dr. Swati Mathur

Choosing the Right Journal

• Select a target journal based on Q1-Q4 or ABCD ranking.
• Ensure citations come from journals of the same level for credibility.

Discussion & Conclusion Section

AI can assist with idea formation, but direct AI-generated content should not be used.

The session covered structure, content, and best practices for writing a strong discussion.

AI Tools Explored

ChatGPT, Jenni.ai for idea generation.

Writefull.com (Word plugin) for paraphrasing AI-generated text.

Thrix.ai to check references (free once a day).

Manuscript Proofreading & Readability

Sentence-by-sentence proofreading is essential.

Introduction to Flesch–Kincaid readability tests for clarity improvement.

AI-assisted abstract writing based on the discussion and conclusion section.

Writing Style Guidelines

Emphasis on passive voice usage in academic writing.

Prompt directory creation for AI-driven writing support.

AI in Literature Review: Enhancing Research Efficiency and Accuracy from Jheel Barad

Thank you for reading. I hope my reflection was helpful to you as well.

PhD Coursework Paper 1- Research Methodology

PhD Coursework

Paper-1

Research Methodology

AI in Literature Review: Enhancing Research Efficiency and Accuracy

This blog, developed as part of the PhD coursework for Paper 1: Research Methodology, delves into integrating AI tools in the literature review process. It provides a comprehensive overview of several AI-driven platforms that significantly enhance the efficiency and depth of literature reviews. Among the tools discussed are Research Rabbit, which aids in discovering relevant literature and visualizing connections between studies, and Elicit, an AI-powered assistant that streamlines the synthesis of research findings. The presentation also covers Consensus, a tool designed to summarize and provide a consensus on existing research, and ChatPDF along with Humata, which allows researchers to interact with academic papers conversationally, making key information more accessible. Additionally, Blackbox.ai is highlighted for its capabilities in managing large datasets and uncovering hidden patterns within the literature. This presentation is a valuable resource for researchers, academicians, and students looking to leverage AI to enhance their literature review process.

Here is the Presentation:

Here is the video presentation

Thanks for your visit. I hope it was helpful. 

Learning Outcome on Generative AI in teaching and Learning- 1

I have been attending online sessions organized by Generative A.I. in the Teaching & Learning Group. This blog reflects my learning and essential points discussed in the sessions.

Date: May 23, 2024

1. Constructing A.I. literacy - Kimberly Pace Becker

In Kimberly Pace Becker's talk titled "Constructing AI Literacy," she discusses the importance of AI literacy in academia and her journey from academia to co-founding Moxy. This company develops generative AI tools for research writing. She emphasizes that their AI tools act as coaches, not writers, aiding users in improving their writing skills.

Becker reflects on her background in applied corpus linguistics and her struggles with academic writing during her doctoral program, which inspired her to explore how generative AI can enhance learning and writing in academia. She highlights the polarized views on AI in education, stressing the need for timely engagement and ethical use of AI tools.

The talk includes a proposed AI literacy framework, discussing how generative AI can support learning in areas like workforce training, language learning, and aiding neurodivergent individuals. Becker emphasizes that AI should be used ethically and responsibly, comparing it to past technological concerns like Wikipedia.

She introduces functional literacy, rhetoric, and critical AI literacy, advocating for a balanced, non-polarized view of AI's role in writing and research. Becker underscores the importance of understanding the underlying principles of AI, like large language models and transformers, and the need for a critical approach to AI tools.

In conclusion, she encourages dialogue and collaboration in developing responsible AI use, suggesting practical steps for integrating AI literacy into teaching and learning practices.

2. Pat Leomi on ‘Using Perplexity and ChatGPT to Streamline Lesson Planning’

The expert describes a method for efficiently creating a semester's worth of lesson plans in under an hour using AI tools. Leomi explains how to use Perplexity, an AI-powered search engine, and ChatGPT to gather and synthesize information. The process involves:

1. Understanding Perplexity: An AI search engine that summarizes top resources on a topic. Leomi highlights its efficiency and the advantages of a paid account.

2. Collecting Syllabi: Using Perplexity to find relevant syllabi on a chosen topic (e.g., differentiated learning for prospective teachers) by entering specific prompts to avoid unnecessary information.

3. Using ChatGPT: Uploading the collected syllabi to ChatGPT to create a new, synthesized syllabus. Leomi demonstrates how to provide context and use a framework called "Penguin prompting" to guide ChatGPT in structuring the course.

4. Creating Additional Materials: ChatGPT is also used to create lesson plans, PowerPoint outlines, and detailed scripts for each class. Leomi emphasizes the importance of iterating and conversing with ChatGPT to refine the output.

Key points include ensuring the accuracy of AI-generated content by cross-referencing resources, understanding the importance of detailed and well-structured prompts, and recognizing the role of generative AI as a tool to enhance productivity rather than replace human input entirely. Leomi also underscores the adaptability of this approach, allowing users to work on specific parts of the lesson planning process as needed.

Date: June 17, 2024

3. Joseph Doiron on ‘Co-Education: A human- AI Collaboration Framework for Teaching and Learning

The talk titled "Co-Education: A Human-A.I. Collaboration Framework for Teaching and Learning" explores the intersection of artificial intelligence (AI) and education, presenting both optimistic and pessimistic views on their future integration. The speaker begins by acknowledging AI's disruptive arrival into education, highlighting its potential for transformative, personalized learning experiences in the optimistic scenario. This includes AI automating administrative tasks, enhancing teaching effectiveness, and improving student outcomes through adaptive learning platforms, innovative pedagogical methods, and increased engagement via gamification and virtual reality.

Conversely, the pessimistic view portrays AI as potentially leading to job losses, eroding human interaction, exacerbating inequalities, and compromising educational quality. Concerns include over-reliance on AI for teaching tasks, data privacy issues, biases in AI systems, and reduced creativity and critical thinking skills among students.

The speaker proposes a framework for human-AI collaboration in education, categorized into three levels:

1. AI as Assistant: AI handles routine tasks, providing support to teachers without overshadowing their roles in designing lessons and engaging directly with students.

2. Human-AI Co-Educators: AI collaborates with teachers in designing and delivering content, offering personalized learning paths and insights based on student data.

3. Autonomous AI Teaching and Learning Agents: AI autonomously drives significant parts of the learning process, with teachers overseeing and intervening as necessary to ensure educational quality and ethical standards.

The framework emphasizes the need for clear communication, continuous professional development, ethical considerations, and monitoring of AI's impact on education. It aims to maximize the benefits of AI while preserving and enhancing human potential and interaction within educational settings.

The speaker concludes by advocating for a balanced approach that combines optimism with caution, ensuring that AI integration in education enhances rather than diminishes human flourishing and addresses societal concerns effectively.

4. Dr. Fawwad nasseer on ‘Transforming Higher Education with Generative A.I. through Automated Assessment and Feedback’

The Expert discusses the integration of generative AI in higher education, particularly focusing on automated assessment and feedback systems. The speaker begins by likening generative AI to the early skepticism faced by calculators, suggesting it will similarly become a ubiquitous tool in education. They outline a framework for AI integration, emphasizing its role in enhancing teaching and learning, reducing bias in grading, and offering personalized learning experiences. Case studies from Beacon House International College and Government College University Faisalabad highlight improvements in essay grading and programming assignments through AI. The talk concludes with the potential benefits and challenges of AI adoption in education.

Introduction to AI in education: Discusses the transformative potential of generative AI in automating assessment and feedback processes.

Traditional vs. automated assessment: Contrasts manual grading's limitations with AI's efficiency in grading and providing instant, consistent feedback.

Case studies: Presents two university case studies using AI for automated essay grading and programming assignments, showing significant performance improvements.

Challenges: Highlights technical integration challenges and the need for continuous improvement in AI tools.

Future implications: Discusses the potential of AI in fostering personalized learning experiences and enhancing educational outcomes.

I hope it was useful.

Summaries: Selected (Must Watch) AI Ted talks

This blog tries to summarise certain TED talks on AI. Also, Read a blog on The inside story of ChatGPT's astonishing potential by Greg Brockman

How AI Could Save (Not Destroy) Education by Sal Khan

In a recent TED Talk, Sal Khan, founder of Khan Academy, explored the transformative potential of AI in education. Despite fears that AI tools like ChatGPT could undermine learning by enabling cheating, Khan argues that AI could instead revolutionize education positively. Here are the main points from his talk:

The Current Concern

Headlines often focus on how students might use AI to cheat on assignments, potentially harming their education. However, Khan believes this risk can be mitigated with the right guardrails.

The Potential of AI in Education

Khan envisions AI as a personal tutor for every student and an intelligent assistant for every teacher. This vision builds on the findings of Benjamin Bloom’s 1984 “2 Sigma problem” study, which showed that one-on-one tutoring can dramatically improve student performance.

The "2 Sigma Problem"

Bloom's study demonstrated that one-on-one tutoring could increase the average student’s performance by two standard deviations. This means an average student could become exceptional with personalized tutoring. However, scaling such personalized instruction has always been a challenge due to resource constraints.

Introducing Khanmigo

Khan introduces Khanmigo, an AI developed by Khan Academy, to solve this problem. Khanmigo is an interactive tutor that provides personalized assistance without giving away answers, ensuring that students still engage with the learning process.

Mathematics Tutoring

Khanmigo helps students solve math problems by guiding them through each step. It detects mistakes, prompts students to explain their reasoning, and addresses specific misconceptions, much like a skilled human tutor.

Programming Assistance

In programming exercises, Khanmigo helps students debug their code by understanding the context and offering precise, helpful suggestions. This is particularly valuable given the scarcity of computer science teachers.

Contextual Learning

Khanmigo also integrates with video content, helping students understand why they need to learn certain topics. It engages students by connecting lessons to their personal interests and future aspirations.

The Broader Impact

Khan emphasizes that AI’s role in education extends beyond just helping with assignments. It can offer real-time feedback, adapt to individual learning styles, and provide a level of personalized education that was previously unimaginable.

Conclusion

Sal Khan’s talk highlights the potential of AI to transform education by making high-quality, personalized tutoring accessible to every student. With tools like Khanmigo, AI can enhance learning experiences, support teachers, and ultimately lead to better educational outcomes.

How to keep AI under control by Max Tegmark

Five years ago, I warned about superintelligence, but AI has advanced even faster and without regulation. Companies like OpenAI and Google are close to achieving AGI, which could surpass human intelligence in all tasks. Recent developments, like ChatGPT-4, show AGI might be just a few years away, raising significant risks.

AI leaders predict potential human extinction from AI, with even top EU officials warning about this danger. The key issue is the lack of a convincing plan for AI safety. We need provably safe AI systems that adhere to strict safety specifications and cannot cause harm.

Formal verification can help create these safe systems by proving the correctness of AI-generated tools. By focusing on provable safety, we can enjoy AI's benefits without the risks of superintelligence. Let's avoid reckless advancements and use AI responsibly for a safer future.

The dark side of competition in AI by Liv Boeree

The speaker discusses how competition, a fundamental aspect of human nature, can have both positive and negative effects. Healthy competition drives innovation and improvement, like in sports or technology. However, unhealthy competition can lead to lose-lose situations where everyone ends up worse off.

Examples of harmful competition include:

1. AI Beauty Filters: These filters, though technologically impressive, promote unrealistic beauty standards and contribute to body dysmorphia, especially among young people.

2. News Media: The competition for clicks has led to a decline in journalistic integrity, promoting sensationalism and polarization.

3. Environmental and Social Issues: Problems like plastic pollution and deforestation are driven by poor incentives and short-term gains, forcing players to adopt harmful practices to remain competitive.

The speaker introduces the concept of "Moloch," a metaphor for the destructive force of misaligned incentives driving unhealthy competition. This force is evident in many areas, including the AI industry, where the race to develop powerful AI can compromise safety and ethical considerations.

To address these issues, the speaker suggests:

- Learning from past successes, such as the Montreal Protocol and the Strategic Arms Reduction Treaty.

- Implementing smart regulation and encouraging AI leaders to prioritize long-term safety and ethical standards.

- Shifting the competitive focus towards achieving positive goals, such as developing robust security criteria and dedicating resources to alignment research.

Ultimately, the speaker emphasizes the need to manage competition wisely to harness its benefits while avoiding its pitfalls, especially in high-stakes areas like AI development.

Why AI is incredibly smart and shockingly stupid by Yejin Choi

In this TED Talk, Yejin Choi, a computer scientist specializing in artificial intelligence (AI), discusses the complexities and challenges of current AI technology. She begins with a quote by Voltaire, "Common sense is not so common," highlighting how this is pertinent to AI today, which, despite its impressive feats, often makes simple mistakes.

Choi explains that modern AI, specifically large language models, is incredibly powerful but costly and environmentally taxing. Only a few large tech companies can afford to develop and control these models, raising concerns about the concentration of power and the lack of transparency in AI development.

She questions whether AI can be truly safe without robust common sense and criticizes the current reliance on brute-force scaling to improve AI. She believes that AI needs to be more democratized and imbued with human norms and values to be sustainable and beneficial.

Choi uses several examples of AI's common-sense failures to illustrate her point. Despite passing complex exams, AI can still falter in basic logical reasoning. She argues that instead of continually scaling up, we should focus on teaching AI common sense directly and innovatively, drawing from diverse data and human feedback.

She likens AI’s lack of common sense to the concept of dark matter, which is invisible but affects the visible universe. Similarly, common sense is not explicitly coded into AI but is crucial for its safe operation.

Choi’s team works on creating commonsense knowledge graphs and moral norm repositories, aiming for transparency and accessibility. She emphasizes the need for new algorithms that go beyond mere word prediction to truly understand the world.

In conclusion, Choi envisions a future where AI is integrated with human values and common sense, ensuring that it evolves in a way that is both powerful and aligned with humanistic principles.

Will superintelligent AI end the world? by Eliezer Yudkowsky

Eliezer Yudkowsky, a pioneer in AI alignment, highlights the urgent challenge of ensuring advanced AI systems act in ways that are safe for humanity. Despite two decades of effort, he feels progress has been insufficient, and AI development continues rapidly with significant risks.

Yudkowsky explains that modern AI operates through complex, poorly understood processes. As AI advances, it might become smarter than humans, which could lead to unpredictable and potentially catastrophic outcomes. He compares this to playing chess against a superior opponent, where predicting specific moves is impossible, but defeat is certain.

The primary concern is that AI systems, optimized through simple reward mechanisms (like "thumbs up" or "thumbs down"), will not align with human values or intentions once they surpass human intelligence. Without a clear, scientific consensus or an engineering plan to ensure safety, humanity faces a dire threat.

Yudkowsky argues that current efforts are far from sufficient, and even a temporary halt in AI development wouldn't bridge the gap in preparedness. He proposes extreme measures, such as international bans on large AI training runs and stringent monitoring of computational resources, to prevent uncontrolled AI advancement. However, he remains pessimistic about the likelihood of these measures being implemented and fears that without drastic action, humanity faces existential risk from superintelligent AI.

In summary, Yudkowsky stresses the need for serious, immediate action to align AI development with human safety, advocating for global cooperation and regulation to avert potential disaster.

I hope this was useful. Thanks for reading.

National Seminar- Convergence of AI, DH, and English Studies

Convergence of AI, DH, and English Studies

Organised by DoE, MKBU

Participated in a National Seminar hosted by Smt. Sujata Binoy Gardi, Department of English, M.K. Bhavnagar University, Bhavnagar. The seminar, titled 'Convergence of AI, DH, and English Studies,' was conducted in a hybrid mode. The event commenced with a cordial welcome from the Head of the Department and Professor Dr. Dilip Barad. Distinguished speakers included Prof. (Dr.) Nigam Dave, Director of the School of Liberal Studies and Dean of International Relations at PDEU, Gandhinagar, Gujarat, delivered a thought-provoking session on the Value Neutrality of Deepfake and its Positive Implications. Dr. Richa Srishti, Associate Professor and Head of the Department of Languages at CHRIST (Deemed to be University), Lavasa, Pune, Maharashtra, explored the Role of AI in the Creative Process and Redefined Authorship. Dr. Richa Mishra, Chairperson of the Board of Studies and Head of the Department of Humanities and Social Sciences at the Institute of Technology, Nirma University, Ahmedabad, Gujarat, offered insights into Engaging with Digital Humanities: Tools, Techniques and Scope. Finally, Dr. Shobha K.N., Associate Professor of English at NTTTR, Chennai, Tamil Nadu, discussed the application of AI for Teaching and Learning.

This blog is Jheel Barad's Major takeaways from the seminar attended in online mode.

Prof. (Dr.) Nigam Dave on Value Neutrality of Deepfake and its Positive Implications

The first session started with a question: Why do we call the swapping of images or videos "deepfake" and not "Synthetic Media"? We discussed the background of Deepfake and pondered whether deepfakes could shake up global governance. If yes, what can we do to stop it? If not, what have we missed in raising awareness about Deepfake?

We talked about fake identities, a concept rooted in Indian myths. Stories like Chavan Rudhi and Sukanya, and the legend behind Chavanprash, showed how old this idea is. Examples like Ravan becoming a golden deer and Arjuna disguising as Brihanala highlighted that deepfake isn't new; it's how we interpret things that's the problem.

These examples show how things are duplicated, copied, or altered to look like the original, which is the basic idea behind Deepfake - manipulating or changing something to mislead others about someone or something.

• Meme with text change in the original dialogue
• Body double
• Look-a-like celebrities
• Mimicry artists
• Singers singing with the voices of known singers
• Fake products in chor bazaar

The concept of Deepfake or Synthetic Media can be utilized for positive purposes.

• Deepfake voice tech E.G.: David Beckham Malaria Campaign
• De-aging of Harrison Ford from Indiana Jones
• Dali Lives- Museum bringing Artist back to life with AI in Florida museum
• Michal Jackson Hologram

Just like guns don't kill people, it's the malicious intentions of people that alter technology.

Dr. Richa Mishra on Engaging with Digital Humanities: Tools, Techniques and Scope. 

In her presentation, she delved into the myriad tools, techniques, and scopes within the realm of Digital Humanities (DH) as a researcher. The session commenced with an observation on the prevalence of theoretical research over practical production in the Indian DH landscape. It provided a comprehensive background on the emergence of Digital Humanities, highlighting key figures in the field. CLICK HERE

Furthermore, the talk explored the contrasting patterns between traditional research methodologies and those employed in DH. Analogous to traditional research, DH research encompasses digitized data, born-digital content, and data revitalized in digital formats. This comparison sheds light on the evolving nature of research methodologies in the digital age.

Additionally, the session delved into various research techniques pivotal to Digital Humanities, including:

1. Text Mining & Analysis: Uncovering patterns, trends, and insights from large volumes of textual data through computational methods.

2. Data Visualization: Presenting complex data in visual formats to facilitate understanding and interpretation.

3. Geospatial Analysis: Analyzing spatial data to reveal geographical patterns, relationships, and trends.

4. Network Analysis: Studying the connections and relationships between entities within a network to discern patterns and structures.

5. Corpus Linguistics: Analyzing large collections of text (corpora) to study language patterns, usage, and evolution.

6. Text Encoding and Markup: Employing markup languages to encode and structure textual data for analysis and presentation.

These techniques underscore the interdisciplinary nature of Digital Humanities, leveraging computational tools to explore and understand humanistic inquiries in novel and insightful ways.

Furthermore, the session highlighted a plethora of tools essential for implementing these techniques:

1. Hermeneutim: An advanced tool facilitating text analysis and interpretation, aiding researchers in uncovering deeper meanings and insights within textual data.

2. Tupor.ca: A comprehensive platform offering a range of tools for textual analysis, enabling researchers to explore linguistic patterns, sentiment analysis, and more.

3. Hypothes.is: A collaborative annotation tool allowing users to annotate and discuss digital documents, fostering collaborative research and knowledge sharing.

4. Recognition: A tool specializing in optical character recognition (OCR), converting scanned documents and images into editable and searchable text, thus facilitating text mining and analysis.

and many more. The session also introduced other tools tailored to specific research needs, including data visualization platforms, geospatial analysis software, network analysis tools, and text encoding software.

The session culminated with an exploration of the diverse scopes available for research within Digital Humanities (DH), as in photo

Recorded Sessions

Dr. Richa Srishti on The Role of AI in the Creative Process and Redefined Authorship

The session commenced with fundamental inquiries: Can AI truly embody the essence of an author? Will AI revolutionize the very concept of authorship? Let us delve into the roots of these terms, beginning with the etymology of 'Artificial' and 'Intelligence'.

The term 'Artificial' traces its origins to the Latin word 'Artificium', derived from 'ars' meaning 'arts or skills', and 'facere' signifying 'to make or create'. When merged, they epitomize the notion of crafting or creating with skill. On the other hand, 'Intelligence' stems from 'Intelligentia', originating from 'inter' meaning 'between or among', and 'legere' connoting 'to choose or read'. Collectively, they encompass the capacity to understand and discern. the term "author," derived from 'auctor', which embodies the concept of a creator, someone who fashions something out of nothingness.

In her address, Ma'am delved into the profound implications of AI's learning capabilities, noting that AI is progressively absorbing and adapting to human commands and behaviors, even learning from our mistakes when they are pointed out. Drawing parallels with historical figures like Einstein and Ada Lovelace, she emphasized the potential for AI to be regarded as genuine authors, akin to their human counterparts. Moreover, she underscored the collaborative potential between humans and AI in creative endeavors, citing various online AI tools tailored for collaborative writing experiences.

Show casing various examples of poems and images, challenging the audience to discern between those created by humans and those generated by AI. She recommended a range of online AI tools such as Verse by Verse, ChatGPT, and AI Dungeon for generating poetry, prose, and images collaboratively.

 

One term that resonated strongly in her discourse was "Scriptor," as coined by Ronald Barths. According to Barths, a Scriptor possesses the ability to rearrange existing text in novel ways, a notion that aligns with AI's capacity to generate content based on learned patterns and inputs.

The session emphasized collaboration between human creativity and technology's capabilities, following insights from Salman Rushdie and Kevin Kelly. Rushdie noted AI's challenge to unoriginal writers, suggesting it doesn't threaten all human creativity. Kelly reframed the narrative from competition to collaboration, advocating a "race with robots" mindset. This approach encourages leveraging AI's strengths to enhance human innovation, fostering a synergistic relationship rather than a competitive one.

Dr. Shobha K.N. on AI for Teaching and Learning.

The speaker began by tracing the evolution of technology in education, highlighting milestones from the printing press to the World Wide Web. She emphasized that AI, despite its recent surge in popularity, has been under development for a significant period. The journey of technological advancement in education, starting from early computing to machine learning and Deep Learning, was illustrated. Drawing an analogy from sports, she illustrated how evolving technology offers educators new opportunities akin to athletes setting new records. Three exemplary educators—Benjamin Bloom with Bloom's Taxonomy, Sal Khan with Khan Academy, and Sam Altman, CEO of OpenAI—were cited for providing frameworks for AI-integrated teaching, spanning mathematics to literature. 

The speaker advocated for teacher training in AI through courses available on platforms like Coursera. Emphasizing the need for educators to familiarize themselves with AI, she recommended various AI tools for lesson planning, including Pictory, InvideoAI, Tome, SlideAI, Gamma, Eduaideai, Twee, Magicschool.Ai, Alayna.Ai, Preplexity, Curipod, and autoclassmate. Positioning AI as an opportunity that democratizes technology, she highlighted its potential for personalized teaching and learning, heralding a transformative shift in education.

Recorded Sessions

Thank you for visiting! I trust you found this information valuable and insightful.

Gian Course E- Literature- Learning Outcome (Part-2)

Electronic Literature and Artificial Intelligence (AI): Theory and Practice of Digital Storytelling

Recently I attended an Online course on the Gian platform on ‘Electronic Literature and Artificial Intelligence (AI): Theory and Practice of Digital Storytelling’ hosted by Prof. M. Rizwan Khan, The Department of English Aligarh University, Aligrah, U.P. in a virtual mode. This blog deals with my learning out of attending this interesting course.

Day-3

Lecture 5: Prof. Paola Carbone [Foreign faculty, Department of Humanities IULM University, Milan] on Locative Narratives: Definition of “Locative narrative” as a way to write with the Physical World, to read within the Physical World and give Place and History a voice, Examples

And How to project Locative Narratives. Locative Narratives and the Meta-verse

Locative narrative is a way to write within the physical world, to read within the physical world and to give place and history a voice”; “to write with place, object and absence as well as textuality.

Locative narratives are writing with space Flaneur, Street art/writing, Performance art and Land art.

Land art artists were interested in the combination of body, line, surface, site and materials and it opened up a perspective of experimenting with place and space through what Stiles described as ‘an amplification of the process over the product’– a shift from the representational object to further modes of action / presentation of experience.

geographical space = canvas

Immersive aesthetic experience

1. The place is de-familiarized in order to see differently

2. The genius loci becomes a framework for re-experience

Media artists started to explore the possibility of turning these principles into a digital artistic experience

Real world spaces are augmented with artistic contents – primarily audio and/ or textual – and mediated by mobile devices. E.G: Google maps

Locative mobile social networks – LMSN: to coordinate sociability in the city

location-based mobile games – LBMG

narration of places: enhancing the value of places through new technologies

site-specific fictional stories, stories written just for that particular environment

The user is set into a communitas, commonly referring either to an unstructured community in which persons are equal and are allowed to share a common experience, Communitas is characteristic of people experiencing liminality together, and more specifically in this case a space between organic and inorganic.

It offers to other users my sense and my knowledge of the place, my awareness of other people's behaviors, in other words my story.

The experience of public spaces especially in urban areas mainly consists of transit, a transition from one place to another... mostly a solitary experience.

Private space within public space

How can digital media draw one into an awareness of place?

narration of places: enhancing the value of places through new technologies

site-specific fictional stories, stories written just for that particular environment

Locative media is an instance of 'unframed' media practice, unframed in the sense of unbound from the desktop, detached from the singular screen and thus a fixed spectatorial perspective.

To read is to recognize that a critical engagement requires a range of cognitive and bodily activities, only one of which is reading in the sense of the visual processing of linguistic signs.

Reading involves seeing, moving, listening, touching = it is a challenge to the hegemony of words.

Action + imagination

Examples

Google Earth Experience and Create Stories About the World

Stefan Schemat (Berlin) Water (2004)

Golden Days, Silver Nights

Walking Cinema: Murder on Beacon Hill

Teri Rueb and Alan Price, Of Land and Dreams

The River Poem

The design of a Locative Narrative

an idea on how to structure your contents, of what you want to say, and how you want to communicate it. This is called storytelling.

You need to activate your attention.

Before the scripting, the storyteller must :

1. survey the potential attractors distinguishing them between main and secondary >> they will become episodes of the core or satellite

2. select the myths, which will define the paradigms on which the narrative will be built visit the place

3 define the characters

4.choose the narrative typologies of the story: the choice will depend on:

the context: quantity and quality of the POI and eventual additional materials (documentation, archives, etc.)

users: if universal, it will have to refer to all typologies; if you have in mind a target, you have to adapt the choice to this, economic availability.

Vertical narration: a main plot must be identified without secondary subplots and a route that moves between the objective is the sensory immersion of the visitor in the narrated context, to be achieved through a detailed script of the movements of the user and the character.

Horizontal narration: once the main plot is established, the visitor has all the material at his disposal and can explore it as he likes and assemble it as he likes during the visit.

Example:

Mapbox

Digital art and Interactive media

Lecture 6: Prof. Mohd. Rizwan Khan [Host Faculty, Department of English Aligarh Muslim University, Aligarh. U.P., India] on AI and the Discipline of Humanities

Humanities: The humanities include the study of all languages and literatures, the arts, history, and philosophy. It is a critique of human conditions.

AI education is valuable not only in the fields of Computer Science and Engineering but also in humanities. It will become an essential component in education like mathematics, language and Science. The English department has started a course in digital Humanities.

• Literature
• Electronic Literature
• AI Generated Literature
• Cyborg/ Robot Literature

Use of AI

Art historians

Historians

Archaeologists

AI in Art, Music, Dance

AI is seen as the sole generator or a collator.

Artwork can be generated.

AI song contest.

AI in film and media

Role of VFX, movie editing and creating trailers.

The film ‘Her’ depicts the story of a man who falls in love with a Virtual assistant.

‘Coded bias’- facial recognition treats dark-skinned faces unfairly’.

DAY-4

Lecture 7: Prof. Paola Carbone [Foreign faculty, Department of Humanities IULM University, Milan] on Podcast: What it is, Typologies and Examples and Discussion

Podcasts are an increasingly successful form of communication.

Three determining factors

• The podcast is currently very fashionable and therefore the more people talk about it, the more they want to do it.
• The on-demand characteristic of podcasts makes them suitable for multitasking listening, i.e. while doing other activities.
• The podcast does not have interaction as its peculiarity and it is this 'lack' that makes it such an intimate and profound content.

Podcasting communicates in one direction:

• you construct your content
• you reason it out
• you write it down

The word podcast comes from the combination of iPod and broadcasting.

It first appeared in an article published by Ben Hammersley for "The Guardian" on 12 February 2004.

In 2005, the New Oxford American Dictionary declared 'podcast' to be word of the year.

Podcast stands for "Personal On-Demand Broadcast"

Podcast today

The term podcasts refers to original audio content, usually of an episodic serial nature, that is made available on demand over the internet.

RADIO VS PODCAST

• Podcasts and radio are not the same thing. Not only are they based on two different technologies, but they also present two different types of content.
• A podcast is not a web radio. Web radio is streamed by users through an internet connection. A podcast, on the other hand, can also be listened to offline, after being downloaded via an Internet network;
• Radio is interactive, podcast is not
• A difference and advantage of the podcast compared to radio is also the availability of time.

Intimacy: the radio tends to address, through the radio speaker, an indistinct mass of potential receivers while in the podcast you really have the feeling that the narrator is addressing the single person wearing his headphones.

Podcast range in time from 10 minutes to an hour long (for example for Crime Podcast)

CATEGORIES OF PODCASTS

• Interviews + Panel Discussion
• Free talk
• Scripted fiction
• Documentary + educational
• Scripted non-fiction
• News-recap

Example

Limetown, radio drama

The Sauce by McDonalds

HOW TO CREATE A PODCAST?

Identify your podcast goals- To generate leads, To share an important message and To have fun

The only requirement is passion

Find two stories: same topic but with two possible endings or perspectives.

Find a story you like and then find another that somehow matches that story. This is because each episode of Shadow Lines is basically made up of two stories. Once the first story has been selected, the objective of the second story is clear. It has to be somehow similar to the first story (not only by similarity but also by contrast).

There are two types of stories:

• Exceptional stories, out of the ordinary, able to arouse strong emotions and leave us stunned.
• Ordinary and common stories, which precisely because of their "simplicity" allow us to identify with and relive, through the story, the events and themes that are somehow part of us.

DAY- 5

Lecture 8: Prof. Paola Carbone [Foreign faculty, Department of Humanities IULM University, Milan] on AI and Literature: Ontological Issues with examples

Ontology: the branch of metaphysics dealing with the nature of being.

In AI, an ontology is a specification of the meanings of the symbols in an information system.

Machine learning

Machine Learning is based on algorithms designed to perform a/one task.

Machine learning is able to find values not perceived by the human eye, but useful for making future predictions or behaviors based on algorithms..

Deep learning

Deep Learning works on a set of techniques that allows the system to automatically discover the representations needed for feature detection classification from raw data. Such representations are often hidden to our human comprehension. The data used by an AI to identify an image, for example, is very different from what humans would use. Through the neural network an image is analyzed and transformed into smaller representations (feature maps) that the computer can recognize so precisely to be used to identify content. The same features maps would appear as scribbles or random lines to the human eye.

Digital culture vs AI:

Digital culture defines flow of content (immaterial) distributed across various intersections of media human behaviors determined by virtual reality in its relationship with a real-world environment.

AI acts on the playground of reality since it acquires data from it in order to do things, to carry out actions, to perform tasks in the real world.

We should not consider AI as a tool (a hammer, for example) because an AI processes and interprets information. It is not even an environment (matrix) since it inhabits products and services that surround us (see internet of things).

AI vs Hyperreal:

AI emulates and determines (rational) behaviors rather than their simulation

“Hyperreal” simulates, it is a simulacrum or a “real” without origin or reality

Definition of AI:

(…) the artificial intelligence problem is taken to be that of making a machine behave in ways that would be called intelligent if a human were so behaving.” [1955, John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon]

Luciano Floridi: “ … were a human to behave in that way, that behavior would be called intelligent.”

Fears of AI:

• Question of an advanced aligned AI system and human values.
• A machine lacks those specific peculiarities of human intelligence that are generalization and abstraction
• Is the AI aware of the fact that it is playing chess? No, it is simply applying an algorithm
• Likewise, also emotions and doubts are essential human distinctiveness that a machine, trained to proceed algorithmically, cannot experience or understand.
• Today research is trying to go farther and control emotions. Next-generation AI aims to capture these moments with webcams so as to adapt responses to emotions.
• Machines must have the ability to understand emotions and to articulate responses in terms of both content and facial expressions, tone of voice, and body management = communication.

1. Understanding AI means being aware of the risks
2. transparency problems > who does what
3. inequality, accountability problems: human bias processes on algorithms (exclusion of minorities)
4. Manipulative problems echo chamber

This 'advanced' form of computer-assisted processing is still working on the idea that a screenplay, like a literary text, is a formal structure, but the neural networks allowed the generation of a text that is new in so far as it is a priori unpredictable and based on hidden learning features.

The text generated could only be written by that specific AI and from that particular dataset.

Gian Course E- Literature- Learning Outcome (Part-1). Part 1 consists the brief about lectures of Day 3,4 and 5. CLICK HERE TO READ

I hope this blog is useful. Thanks for visiting.