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Examples of generative AI use in colleges and universities.

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Create Essay PlansNorth West Regional College (FE)

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Jisc (2025) Strategic Framework for AI in Colleges

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Jisc (2025) Strategic Framework for AI in Colleges

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A Strategic Framework for AI in Colleges and Universities - Artificial intelligence

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Artificial intelligence is already part of the everyday reality of colleges and universities, shaping how students learn, how staff work, and how institutions operate. Across the board, we see institutions moving along our AI maturity model towards embedding AI. That makes leadership and strategy essential. We are working on a new framework to help the […]

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An example of a robust AI policy - ICO, 2026:  Internal AI Use Policy

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An example of a robust AI policy - ICO, 2026:  Internal AI Use Policy

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A useful example from the UK Data Commissioner's Office of a typical organisational policy for AI use.

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One Trust: Governing AI in 2026: A global regulatory guide

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One Trust: Governing AI in 2026: A global regulatory guide

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How major AI laws in Europe, the United States, APAC, and Latin America define risk, accountability, and documentation requirements across the AI lifecycle

Why privacy and compliance teams are central to AI governance, and how existing privacy workflows extend into AI risk assessments, transparency, and monitoring

What it takes to operationalize AI readiness, from inventories and impact assessments to consistent disclosures and evidence for regulators

Use this guide to assess your current AI governance posture, identify gaps across jurisdictions, and prepare for enforcement-driven scrutiny in 2026.

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Governing AI in 2026: A global regulatory guide

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Download “Governing AI in 2026”, a global regulatory guide for privacy and compliance teams navigating AI laws, enforcement trends, and operational AI governance requirements.

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EU / European Training Foundation Ai Literacy Framework for Students (2025)

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EU / European Training Foundation Ai Literacy Framework for Students (2025)

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AmplifyFE Innovative use of AI in FE case studies

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AmplifyFE Innovative use of AI in FE case studies

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AI focused case studies for 2024 We are delighted to launch the latest AmplifyFE Insights Case Studies, at the ALT ’Harnessing AI in Further Education: Case Studies and Future Perspectives Sy…

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Get in touch to enquire about the courses below or bespoke options

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Get in touch to enquire about the courses below or bespoke options

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Introduction to GenAI Taster session (3 hours)

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Introduction to GenAI Taster session (3 hours)

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Key Learning Areas:

By the end of the course all delegates will be able to:

Understand fundamental AI concepts and terminology.

Identify practical AI applications for home and business.

Describe the purpose of a some genAI tools including ChatGPT, Microsoft Co-Pilot, Google Gemini

Use basic techniques to create a range of documents and communications relevant for own purposes

Evaluate the potential impact of AI on individuals and society.

Formulate an action plan for own genAI use and development.

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GenAI for Business (6 hours or 2 x half-days)

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GenAI for Business (6 hours or 2 x half-days)

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Key Learning Areas:

By the end of the course all delegates will be able to:

Describe the purpose of a range of genAI tools for business including ChatGPT, Microsoft Co-Pilot, Microsoft Translate

Evaluate the likely benefits and costs of using different versions of genAI and how up-to-date they are

Use prompt engineering techniques to create a range of business resources relevant for own organisation

Evaluate some of the disadvantages of genAI use including ethical and data protection issues and the risk of ‘hallucinated’ responses

Describe likely future developments in genAI

Formulate an action plan for own genAI use development and organisational development and policy

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UK AI Strategy Beyond 2024 (Demos, 2024)

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UK AI Strategy Beyond 2024 (Demos, 2024)

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Includes summaries of original 2021 Strategy and 2023 updates.

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Uses text input and has a free account option with limited time (3 mins/month) as well as costed options.

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AI Image Generator: AI Picture & Video Maker to Create AI Art Photos Animation | Deep Dream Generator DDG

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With Deep Dream Generator, make amazing AI art, photos, videos in seconds! Unleash your creativity - and join DDG's vibrant online community of AI artists!

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Living and Working with AI - recommended by David, thank you!

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**A New York Times Bestseller** ‘Co-Intelligence is the very best book I know about the ins, outs, and ethics of generative AI. Drop everything and read it cover to cover NOW’ Angela Duckworth Consumer AI has arrived. And with it, inescapable upheaval as we grapple with what it means for our jobs, lives and the future of humanity. Cutting through the noise of AI evangelists and AI doom-mongers, Wharton professor Ethan Mollick has become one of the most prominent and provocative explainers of AI, focusing on the practical aspects of how these new tools for thought can transform our world. In Co-Intelligence, he urges us to engage with AI as co-worker, co-teacher and coach. Wide ranging, hugely thought-provoking and optimistic, Co-Intelligence reveals the promise and power of this new era.

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Perplexity.AI -  Retrieval Augmented Generation (RAG)

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Perplexity.AI -  Retrieval Augmented Generation (RAG)

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Uses Retrieval Augmented Generation (RAG)

When a user submits a query, first it searches the web for verifiable information, trusted sources

This search information is combined with the original query to create an enriched prompt

It uses different AI models including ChatGPT and Clause depending on the task

It generates a ‘grounded’ response based on the AI’s knowledge and verified external information

It cites the sources it used.

Less prone to hallucination and provides verifiable sources.

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Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.

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Fitzpatrick (2023) PREP prompt engineering model and from #TheAIClassroom

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Fitzpatrick (2023) PREP prompt engineering model and from #TheAIClassroom

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Prompt craft guide for generating images

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Prompt craft guide for generating images

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Described as for younger earners but I love this for all ages!

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From Google - sets out ideas behind GoogleLM and its purpose and uses

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We’re rolling out NotebookLM, an experimental offering from Google Labs to summarize information, complex ideas and brainstorm new connections.

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GoogleLM - writing and research assistant

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GoogleLM - writing and research assistant

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LM = Language Model

New in late 2024 this free tool can:

Answer questions and offer key insights on complex documents you upload

Convert complex material into FAQs, briefing documents, audio such as podcasts

Store notebooks of key resources for sharing and as group knowledge bases

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Not your computer? Use Guest mode to sign in privately. Learn more about using Guest mode

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Download offline translation packs before your next adventure and translate while off the grid.

Real-time, translated captions

Translate a live conversation into one of over 60 languages directly from the app.

Presentation Translator for PowerPoint

Caption & translate live presentations while letting your audience follow along in their language.

Translator for Education

Microsoft Translator helps bridge communication gaps by supporting accessible classroom learning with live captioning, cross-language understanding, and even multilingual casual conversations to help with student integration.
VISIT THE EDUCATION PAGE

Translator for Business

Learn more about how Microsoft Translator's text and speech translation technology can globalize your business and connect with your customers worldwide.
VISIT THE BUSINESS SITE

Translator Blog

Check out the latest news and stories from Microsoft Translator.
VISIT THE BLOG

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Microsoft Translator live is a free translation and transcription service that lets you have translated conversations across multiple devices on iOS, Android, Windows, PowerPoint and web for 1:1 conversations or larger group interactions. Support for real time conversation for over 60 languages including Spanish, French, Chinese, Japanese and many more.

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Arizona State University Open Source Voiceover Generator 

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Arizona State University Open Source Voiceover Generator 

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Voiceover Generator Access If you have issues using the generator on this page, use the following link to access the voiceover generator on a new page. Loading… Walkthrough Video Coming soon!

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A philosophy for AI-enhanced times: Braidotti on 'Posthumanism'

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A philosophy for AI-enhanced times: Braidotti on 'Posthumanism'

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Includes discussion on the 'Vitruvian Man' ideal of humanity

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Summary of Rosi Braidotti’s The Posthuman (Part 1)

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For people interested in Critical Posthumanism, Rosi Braidotti’s The Posthuman is probably a good place to start. Throughout the book…

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Nikolai Elkins (2023): using an intersectionality approach to prompts

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Nikolai Elkins (2023): using an intersectionality approach to prompts

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Part of the wonderful 'Digitally Enabled Educator' webinar series

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Evaluating Unit Briefs with Custom ChatGPT Instructions - Nikolai Elkins

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Writing inclusive prompts - keep intersectionality (Crenshaw) in mind

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Writing inclusive prompts - keep intersectionality (Crenshaw) in mind

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What is intersectionality and why is it important in the workplace? - Inclusive Employers

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What is intersectionality? First outlined and defined by Dr. Kimberley Crenshaw in her 1989 paper, ‘Intersectionality’ was originally used to describe and explain the experience of Black women. However, it has evolved since then and is now often used to describe a myriad of intersecting and marginalised identities. Dr. Crenshaw highlighted, and gave name to,…

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The Hidden Human Labour Powering AI

James Muldoon, Mark Graham & Callum Cant

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Big Tech has sold us the illusion that artificial intelligence is a frictionless technology that will bring wealth and prosperity to humanity. But hidden beneath this smooth surface lies the grim reality of a precarious global workforce of millions that labour under often appalling conditions to make AI possible. Feeding the Machine presents an urgent, riveting investigation of the intricate network of organisations that maintain this exploitative system, revealing the untold truth of AI.

Based on hundreds of interviews and thousands of hours of fieldwork over more than a decade, this book shows us the lives of the workers often deliberately concealed from view and the systems of power that determine their future. It shows how AI is an extraction machine that churns through ever-larger datasets and feeds off humanity's labour and collective intelligence to power its algorithms. Feeding the Machine is a call to arms against this exploitative system and details what we need to do, individually and collectively, to fight for a more just digital future.

"[A] call to arms to take control over our digital futures: build worker power, hold big tech accountable, and create a better understanding of how these systems work"
financial Times

See more reviews

" Feeding the Machine may just be the most important book to be written in the current fever of AI publishing. It shines a light into the darkest corners of this 'revolution', revealing the enormous human cost behind the giant servers and grinding labour farms that exploit and abuse the human spirit in order to provide a technology that itself exploits and abuses the human spirit. I urge anyone who uses, harnesses or leverages AI to read this and consider. What an important book"
Stephen Fry

"I had no idea of the dark, tangled world of human exploitation and corporate greed that is fuelling the growth of AI. Here we meet people working impossible hours for pitiful wages - just so we can get our orders a couple of hours earlier, while some billionaire gets richer. If you think - as I once did - that the internet is a kind of 'free lunch' you need to read this extraordinary and essential book"
Brian Eno

"The darker side of the shiny AI era is the subject of Feeding the Machine ... The authors delve into seven archetypal jobs in the AI supply chain"

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Feeding the Machine by James Muldoon, Mark Graham, Callum Cant - Canongate Books

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Feeding the Machine - The Hidden Human Labour Powering AI by James Muldoon, Mark Graham, Callum Cant - hardback (9781837261819) published by Canongate.

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The University of Kent's Digitally Enhanced Education monthly webinars. AI, digital skills, technology enabled learning and more!

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Welcome to the University of Kent's Digitally Enhanced Education webinars! These webinars are an opportunity to share examples amongst colleagues when using digital technologies for teaching. We hope that you will find some great ideas.

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This Padlet, created by Lynne Taylerson, @realtimeedu is a board for sharing developments in AI and suggesting how they can be leveraged to help teachers working with adult learners, both by the teacher or the learners.

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This Padlet, created by Lynne Taylerson, @realtimeedu is a board for sharing developments in AI and suggesting how they can be leveraged to help teachers working with adult learners, both by the teacher or the learners.

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The basic versions of tools and platforms linked to here are free with paid options available with extra functionality for many tools. Efforts have been made to signpost to Open Source of free tools wherever possible to make this resource valuable for teachers with low or no budget to investigate AI deployment.

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Follow me on socials and find out more about my practice and research

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Follow me on socials and find out more about my practice and research

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Links to my social media presence, doctoral research, current research, published work and my company, Real Time Education.

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Real Time Education Ltd, FE educator, mentor, author, digital learning designer

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NCFE / OU (2024) Generative AI and the Future of Assessment

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NCFE / OU (2024) Generative AI and the Future of Assessment

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Our Assessment Innovation Fund pilot with The Open University | NCFE

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Our Assessment Innovation Fund pilot with The Open University

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Find out more about our Assessment Innovation Fund pilot with The Open University.

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UNESCO (2024) AI Competency Framework for Students

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UNESCO (2024) AI Competency Framework for Students

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UNESCO (2024) AI Competency Framework for Teachers

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UNESCO (2024) AI Competency Framework for Teachers

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Fitzpatrick (2023) EDIT AI response accuracy checking model from #TheAIClassroom

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Fitzpatrick (2023) EDIT AI response accuracy checking model from #TheAIClassroom

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Series of guides on prompt craft, using major models (GPT, Gemini) and more

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Empowering educators Archives - National centre for AI

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A series of guides part 4 of 4 Welcome to the concluding instalment of our Empowering Educators by Harnessing Generative AI tools. Google Bard was part of Google's LaMDA family of large language models, Bard has undergone significant evolution, transitioning first to the PaLM model and subsequently to Gemini.

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Generative AI in Higher Education King's College London 2-week MOOC

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Generative AI in Higher Education King's College London 2-week MOOC

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2-week, 8 hour free MOOC giving a really good introduction to AI definitions, tools, use, challenges and issues for educators.

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Many short video guides on common applications (Teams, whiteboard options) plus reviews and guides on emerging AI such as Gemini and MS Co-Pilot

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edtech free apps videotutorials videoguides

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Welcome to my channel it is packed full of how to guides on using various online tools and apps to support teaching and learning. If you are looking for ideas of technology to use in the classroom then this is the place to come. With new videos uploaded regularly.

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Covers basic operations and ideas for use

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Integrated into Microsoft 365 licenses, Co-pilot can help create documents such as PowerPoint or Excel spreadsheets presentations automatically via text prompts or document uploads. It can create photo-realistic images and describe images that you upload to it. It also has a chat facility which will answer questions, summarise websites or document content and create code.

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Microsoft Copilot is your companion to inform, entertain and inspire. Get advice, feedback and straightforward answers. Try Copilot now.

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Napkin AI - summary and infographic generator

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Napkin AI - summary and infographic generator

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Free unlimited access during current Beta test phase (winter 2024) with reduced cost options for testers and limited free options later.

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Join Napkin.ai and start creating visuals for your ideas today. Napkin turns text content into engaging visuals for presentations, social media, docs and more.

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Published by Teachergoals.com with supporting newsletter and social media presence. Frameworks for AI pedagogy and evaluation as well as a glossary of useful tools.

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The AI Classroom | TeacherGoals Publishing

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What is the role of educators in the artificial intelligence revolution? The AI Classroom will equip educators with the knowledge and skills needed to stay ahead of the curve and fully embrace the transformative power of artificial intelligence.

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Publishing an intro to generative AI is a challenge as things are moving so quickly. However, we think things have now settled down enough for us to bring together information in a single place, to create a short primer. This blog post will be updated as needed, and we have also produced a version as a more formal report.
Version 1.3 - 2nd Jan 2024. First version published in April 2023.

Table of Contents
2. An Introduction to the Generative AI Technology 2.2. Microsoft Copilot 2.3. Google Bard 2.4. Other Models 2.5. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models 2.6 Image Generation 2.7 Beyond Chatbots 3. Impact of Generative AI on Education 3.1 Assessment 3.1.1 Guidance on advice to students 3.1.2 The role of AI detectors 3.2 Use in Learning and Teaching 3.2.1 Examples of use by students 3.2.2 Examples of use by teaching staff 3.2.3 Examples of uses to avoid 3.3 Adapting curriculum to reflect the use of AI in work and society.
1. Introduction

Generative AI tools such as ChatGPT are already having a significant impact on education. These tools pose considerable challenges around assessment and academic integrity, but also present opportunities, for example saving staff time by helping with the creation of learning materials or presenting students with new tools to enhance the way they work. The impact of generative AI is being felt far beyond education, and it is already starting to change the way we work. This presents more challenges and opportunities, in making sure education prepares students for an AI-augmented workplace, and that assessments are authentic yet robust.

The primer is intended as a short introduction to generative AI, exploring some of the main points and areas relevant to education, including two main elements:

An introduction to Generative AI technology

The implications of Generative AI on education

2. An Introduction to the Generative AI Technology

Whilst we do not need a detailed technical understanding of the technology to make use of it, some understanding helps us understand its strengths, weaknesses, and issues to consider. In this section, we will be looking into the technology in a little more detail.

This is a fast-moving space, and the information here is likely to age quickly! This edition was updated in January 2024, and we aim to update it regularly to take into account significant developments.

This guide starts by looking at AI text generators, also known as Large Language Models (LLMs).

2.1 ChatGPT

ChatGPT has grabbed most of the headlines since its launch in November 2022. It was created by a company called OpenAI, which started as a not-for-profit research organisation (hence the name) but is now a fully commercial company with heavy investment from Microsoft. It is available as a free version, plus a premium version at $20 a month, which provides faster, more reliable access, as well as access to its latest language models and features, including GPTs, which changes its behaviour significantly.

ChatGPT is based on a machine learning approach called 'Transformers', first proposed in 2017, and is pre-trained on large chunks of the internet, which gives it the ability to generate text in response to user prompts, hence the name 'Generative Pre-trained Transformer'. Whilst OpenAI provided some information on the approach for training ChatGPT, they haven't so far, released any information about GPT4, the latest model released in early 2023.

In its standard mode, without plugins, ChatGPT works by predicting the next word given a sequence of words. This is important to understand, as it is not in any sense understanding your question and then searching for a result and has no concept of whether the text it is producing is correct. This leads it to be prone to producing plausible untruths or, as they are often known, hallucinations.

As it stands today, the free version of ChatGPT doesn't have access to the internet, so can't answer questions beyond its training data cut-off date of January 2022. Users paying for the ChatGPT Plus service have access to a version that can access the internet, plus a later cut-off date of April 2023 for pre-trained information.

ChatGPT Plus customers also have access to GPTs which extend ChatGPT's functionality. This is replacing an earlier tool called 'Plugins', and allows any user to create a custom version of ChatGPT, focused on a particular task.

OpenAI makes its service available to other developers, so many other applications make use of it, including many writing tools such as Jasper and Writesonic, as well as chatbots in popular applications such as Snapchat.

2.2. Microsoft's Copilot tools

Although ChatGPT has got most of the hype, there are other players in this space, and this number is likely to increase. Microsoft have several products, all based on OpenAI's technology, and variations of the name 'Copilot'

Of most interest to education at the moment is Microsoft Copilot (which was called Bing Chat), which is based on Open AI's GPT-4. It's a chat tool focused on searching for information and does have access to the internet. It takes your question, performs one or more web searches based on your question, and will then attempt to summarise and answer, giving references for the sites it uses.

There is also an enterprise edition - this version is likely to be highly attractive to universities and colleges, as it ensures data and information included in the chats doesn't leak outside your institution.

Microsoft has also introduced Microsoft Copilot for 365 - a set of generative AI tools for Microsoft 365 (i.e. Word, Excel, PowerPoint etc). These are currently only available to institutions wishing to sign up a minimum of 300 users at $30 a month.

These tools, along with a similar set from Google, move generative AI from a separate concern to part of 'everyday AI' - tools our staff and students use every day.

2.3 Google's Bard

Bard is Google's equivalent of Microsoft Copilot, and is powered by Google's own AI models. Like Copilot, it can access the internet, but unlike Copilot, it doesn't provide references for the sites it used to give its answers, at least in its initial response. It does however have an interesting 'fact-checking' feature, where it will search the web for similar content to back up its answers and show you where it found the information.

2.4 Other models

There are quite a few other models under development - Claude and Llama being two of the more notable.

Claude is similar to ChatGPT and is produced by Anthropic. It is likely to be built into many applications going forward.

Meta's Llama is slightly different in that it has been made available as an open-source model, meaning that you can run it yourself. Open-source AI models often differ from open-source software though, and it's not possible to fully understand how the Llama model works, or modify it yourself, from this release.

2.5. A summary of key capabilities, limitations, and concerns around ChatGPT and other Large Language Models.

In considering generative AI, it's important not only to understand its capabilities but also its limitations. We also believe users should have a broader understanding of the societal impact of generative AI. Some of the key themes are summarised here:

It can generate plausible but incorrect information.

ChatGPT is only trained on information up until January 2022 (but those with the paid ChatGPT Plus service have access to a version that can access the internet, and has a slightly later training date)

Limited ability to explain the sources of information for its responses (this varies between Chatbots)

2.6 Image Generation

It's not all about text - image generation tools have made huge progress too, particularly with Midjourney, DALL-E and Stable Diffusion.

As for the text generators, these have been trained on information scraped from the internet, and there is a lot of concern about the copyright of the training material.

These work in a similar way to text generators - the user gives a prompt and one or more variations of images are produced.

Image generation capabilities are being incorporated into general AI services, so Microsoft Copilot, for example, can also generate images, using OpenAI's DALL-E.

2.7 Beyond Chatbots

Whilst the focus has been on chatbots and image generation, generative AI is finding its way into a wide range of tools and services that can potentially be used in education. As an example, Teachermatic can create a range of resources for teachers, including lesson plans and quizzes, Gamma can create websites and presentations, and Curipod can create interactive learning resources. Many of these applications are built on tools provided by OpenAI and powered by the same technology as ChatGPT.

3. Impact of Generative AI on Education

The impact of generative AI in education is still unfolding and is likely to for some time to come. Key areas include assessment and academic integrity, its use in teaching and learning, use as a time-saving tool, and use by students.

Jisc has convened a number of working groups with representatives from universities and colleges to help us collate and present more detailed advice in these areas, particularly around sector-level advice, assessment and advice to students. One of the first outputs of this work - Assessment Menu: Designing assessment in an AI enabled world - is now available.

Here we will now give an overview of the core themes.

3.1 Assessment

Initial discussions about generative AI have focused on assessment, with the concern that students will use generative AI to write essays or answer other assignments. This has parallels with concerns around essay mills. These concerns are valid, and whilst essays produced wholly by generative AI are unlikely to get the highest marks, their capability is improving all the time. The ability isn't just limited to essays. ChatGPT is also highly capable of answering multiple-choice questions and will attempt most forms of shorter-form questions. It will often fall short, especially when answers are highly mathematical, although this will not be obvious to the student using the chatbot service.

There are three main options, each with their own challenges. There is broad acceptance that 'embrace and adapt' is the best strategy in most instances.

Avoid
Revert to in-person exams where the use of AI isn't possible

This moves away from authentic assessment and creates many logistical challenges.

Outrun
Devise an assessment that AI can't do.

AI is advancing rapidly and given the time between the assessment being set and it being taken, AI might well be able to do the assignment when it is taken.

Embrace and adapt
Embrace the use of AI, discuss the appropriate use of AI with students, and actively encourage its use to create authentic assessments

Balancing authentic assessment and the use of generative AI with academic integrity is a challenge.

The immediate action is for all staff to engage with generative AI and try it themselves, learning how their assessments will be impacted. Alongside this, institutions will need to consider their strategic approach to AI, review, and update policies, and communicate guidance to students.

For higher education, this aligns with guidance provided by QAA, and for both higher education and further education, general guidance has been provided by the Department for Education. In addition, the Joint Council for Qualifications has produced guidance for protecting quality of qualifications.

3.1.1 Guidance on advice to students

We are seeing the guidance to students being published by many institutions. Getting the wording on this can be challenging, and is discussed further in a Jisc blog post ' Considerations on wording when creating advice or policy on AI use '. Our key messages are:

Don't try to 'ban' specific technology or AI as a whole.

Describe acceptable and expected behaviours and provide examples.

The detail of advice will vary between subjects, so supplement general advice with tailored advice for the subject.

We will shortly be publishing example guidance, particularly for colleges.

3.1.2 The role of AI detectors

Discussions about academic integrity inevitably include the role of AI detection software. There are a number of tools in this space, but for most UK universities and colleges, Turnitin's newly released AI detection tool will be the obvious tool to consider. The key things are:

No AI detection tool can conclusively prove that text was written by AI.

These tools will produce false positives.

The tools won't be able to differentiate between legitimate and other uses of AI writing tools.

Best practice for using such tools is still being considered and developed across our sectors, and we will aim to provide examples as these develop.

A Jisc blog post explores the concepts and considerations around AI detection in more detail, and another gives our latest recommendations.

3.2 Use in Learning and Teaching

We are seeing a wide range of ideas for how to use generative AI in learning and teaching. It's worth remembering at this point that it is a fast-moving space and many of the tools are still under development or at the beta stage. Whilst exploration makes a lot of sense, it's also worth noting that individuals shouldn't feel the need to rush into using it in teaching - things are changing rapidly, but now is definitely the time to explore and start planning.

3.2.1 Examples of use by students

We welcome the fact that student voices have been brought into the discussion around generative AI, for example ' Generative AI: Lifeline for students or threat to traditional assessment?'. Jisc has produced a report on the findings of student panels sponsored and hosted by the University of Manchester.

Examples of uses by students.

To formulate ideas, for example, creating essay structures

Generative AI tools are generally effective in producing outlines as a starting point for an assignment.

To provide feedback on writing

Generative AI will proofread and correct text for students, in a similar way to grammar tools. It will also provide feedback on style and content. Students will need clear advice on when this should be declared.

As a research tool

A good understanding of the tool and its limitations is crucial here, particularly the tendency for generative AI to give misinformation.

Generating images to include in assignments.

The best image-generation tools come at a cost, and students need to be aware of copyright concerns.

In section 2.3 we noted that digital inequality is a concern. For example, students who pay for ChatGPT Plus get faster, more reliable access, as well as access to the latest model, and those who pay, for example, for MidJourney image generation will get more and better images than most of the free options. At the moment there are limited options for licensing these tools institutionally, but we expect this to change, and consideration should then be given to licensing options to avoid inequality.

3.2.2 Examples of use by teaching staff

If used appropriately, generative AI has the potential to reduce staff workload, for example by assisting with tasks such as lesson plans, learning material creation, and possibly feedback. The key words here are 'appropriately' and 'assisting'. In section 2.3 we noted some of the limitations of generative AI, such as incorrect information, so as things stand today, generative AI can assist by, for example, generating initial ideas, but its output should always be carefully reviewed and adapted.

Use of generative AI may be by directly using a general chat app such as ChatGPT, or maybe via an application built on top of generative AI, such as Teachermatic. In the former case in particular staff will need good prompting skills to make the most of generative AI.

Drafting ideas for lesson plans and other activities

The output may be factually incorrect or lack sound pedagogical foundations. Nonetheless, it may be a useful starting point.

Help with the design of quiz questions or other exercises.

Generative AI can quickly generate multiple-choice quizzes and assessment ideas, but they should be reviewed carefully as above.

Customising materials (simplifying language, adjusting to different reading levels, creating tailored activities for different interests)

Generally, when asked to customize material, generative AI won't introduce new concepts, and so is less likely to introduce factually incorrect information.

Providing custom feedback to students.

At the moment, generative AI should not be used to mark student work, but it can be a useful tool for assisting with personalized feedback.

The Department for Education has recently published the results of a call for evidence on the use of generative AI in education. which shows ways in which respondents are already benefitting from generative AI, alongside their concerns.

3.2.3 Examples of uses to avoid

In general, you should avoid posting any work that isn't your own, and any personal information into ChatGPT, as it might well be used as part of the training data.

Things get a little more complex when considering tools built with ChatGPT, and it is almost certain that generative AI built into tools like Microsoft 365 will have the same data privacy protection as the rest of Microsoft 365.

Marking student work

There is no robust evidence of good performance, although ChatGPT will confidently do this if you ask.

Detecting whether work is written by AI

ChatGPT might claim it can detect whether it wrote the text, but it can't

Anything involving personal information

You should never put personal information into any system that your University or College hasn't got a proper contract in place with and made a full assessment of its data privacy policies etc. Generative AI services like ChatGPT are no exception.

3.3 Adapting curriculum to reflect the use of AI in work and society.

We are seeing a broad acknowledgement that work will change too, but understandably limited action around this at the moment.

OpenAI has done some initial research on the impact of LLMs and jobs and has estimated that 'around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted.' A 2021 report by the Department for Business, Energy and Industrial Strategy looks more broadly at jobs affected by AI, both in terms of those likely to be automated and growth areas.

In the short term, we expect generative AI to be quickly adopted into courses where it quickly becomes the norm in the workplace. For example, visual arts make use of generative AI, and computer coding makes use of generative coding tools.

4. Regulation

Given the pace of change, regulation has struggled to keep up. In the UK, the government has published an AI white paper which aims to balance regulation and enabling innovation. The paper makes explicit reference to generative AI and large language models. The US government have produced a Bill of Rights and the EU's AI act is working its way through the European Parliament. The EU AI act classifies the use of AI in education as high risk, putting additional responsibilities on providers. The proposed UK approach will look to regulate the use of AI, rather than AI technology itself, through the work of existing regulators, against a common set of principles. As progress is made in this area we will endeavour to highlight any areas of particular concern for education.

5. Summary

Generative AI is progressing rapidly and is likely to have a significant impact on education for the foreseeable future. Keeping up with the advances is a challenge, and balancing authentic assessment and academic integrity is increasingly complex. Nonetheless, with care and an increase in staff and student knowledge, there are substantial gains to be made. This guide aims to give a broad introduction to generative AI. Much more has been written on the topic, and for those who wish to explore further, we have included a range of resources for further reading.
Keeping updated:
To keep up to date with the work of Jisc's National Centre of AI in Tertiary Education, join our Jisc mail list: aied@jiscmail.ac.uk.
Further Reading:
Generative artificial intelligence in education - Department For Education (March 2023)

Generative AI in education Call for Evidence: summary of responses Department For Education (Noc 2023)

Maintaining Quality and Standards in the ChatGPT Era: QAA Advice on the Opportunities and Challenges Posed by Generative Artificial Intelligence - QAA (May 2023)

Reconsidering assessment for the ChatGPT era - QAA (Jul 2023)

ChatGPT and Artificial Intelligence in higher education - Unesco (April 2023)

Guidance for AI in Education and Research - Unesco (Sep 2023)

Artificial Intelligence (AI) Use in Assessments: Protecting the Integrity of Qualifications - JCQ (March 2023)
Selected relevant Jisc Blog Posts:
Staff and student use:

Generative AI: Lifeline for students or threat to traditional assessment? (April 2023)

Means. Motive, Opportunity: A Composite Narrative about Academic Misconduct (March 2023)

Generative AI and How Students Are Using It (June 2023)

Institutional Policy

Considerations on wording when creating advice or policy on AI use - (Feb 2023)

Navigating Terms and Conditions of Generative AI (Sept 2023)

Licensing Options for Generative AI (Dec 2023)

AI Detection:

AI Detection - Latest Recommendations (Sept 2023)

A short experiment in defeating a ChatGPT detector (Jan 2023)

Bias and other ethical considerations:

Hidden Workers powering AI (March 2023)

Exploring the potential for bias in ChatGPT (Jan 2023)

Using Generative AI:

Getting started with ChatGPT Part 1 - Understanding ChatGPT - (March 2023)

How to Explore AI Image Generation (Nov 2022)

Debunking Myths and Harnessing Solutions: Navigating generative AI in Education (Dec 2023)

Change log

V1.1 - 22 May 2023

We've added a table of contents.

Section 2.1 (ChatGPT) has been updated to reflect the fact that all ChatGPT Plus users have access to plugins and web browsing.

We've added a CC BY-NC-SA license

V1.2 - 16 Oct 2023

References to DALLE-2 changes to just DALL-E with the release of DALL-E 3.

Section 2.2 updated to reference Llama and Bing Chat Enterprise.

Section 2.2 Reference to Claude and Bard being behind ChatGPT removed as they have progressed signficantly.

Section 3 added linked to Assessment Menu

Section 3.1.2 - added link to latest Jisc AI detection blog.

Section 3.2.1 - added link to report on student forums.

Section 4 - updated to reflect AIEd classed as high risk in EU Act.

Further reading - added recent documents from QAA and Unesco.

Blogs list - date of articles added

V1.3 - 2 Jan 2024

References to Bing Chat changed to Microsoft Copilot as Micrsoft have renamed the product.

Section 2.1 - Replaced text on ChatGPT plugins with text on GPTs.

Section 2.2 Merged all content about Copilot into its own section to reflect Microsoft's latest branding

Section 2.2 Updated content on Microsoft Copilot to reflect pricing of Copilot 365.

Section 2.3 Moved Google Bard into its own section 2.3 as it continues to evolve. Updated the screenshot and text to show the fact-checking feature.

Section 2.4 Moved Claude and Llama into a section '2.4 Other Models' - this was previously section 2.2.

Section 3.2.2 Added reference to DfE call for evidence report.

Further reading - added recent document DfE

Blogs list updated

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A Generative AI Primer - National centre for AI

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The primer is intended as a short introduction to generative AI, exploring some of the main points and areas relevant to education, including two main elements: an introduction to Generative AI technology and the implications of generative AI on education

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The CREATE framework for writing AI prompts (Birss, 2024)

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The CREATE framework for writing AI prompts (Birss, 2024)

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From the course: How to Research and Write Using Generative AI Tools

How to write an effective prompt for AI

(keys typing) (bell dings) - There is an art to writing a good prompt and I'll be sharing an easy framework that will help you become a better prompt engineer. That's the term for it. Let's understand that ChatGPT is drawing on a massive dataset taken from numerous sources. This covers lots of topics and ranges from great quality to low quality. The job of a great prompt is to focus in on a specific topic and to try and narrow in on the good quality data. I'll explain how we can go about doing that. It just so happens that the framework follows an easy mnemonic, CREATE. Let me take you through each of the letters in turn. C is for character. It's a good idea to tell ChatGPT what role you want it to play. So I'll often say something like, you are a talented copywriter with 20 years experience writing high-performing sales copy. That tells the AI what information it should draw on, not just zeroing in on sales copy, but drawing on the best sales copy. So imagine that ChatGPT is an actor and you're letting it know its role and motivation. Now we've got R for request. This is where we tell the AI what we want it to do. I recommend that you're specific here so it's not just write me a sales email for a sports car. You know, it should be giving context and useful information. It'd be much better to say something like, write me a persuasive and enthusiastic sales email for the Cougar Hyper Sport, totally made up. This is a high-performing electric sports car with the best acceleration in its category. The sales letter should tell people they need to experience it for themselves, find out what it feels like to be pinned to your seat, to feel the heart-thumping exhilaration of cornering, and to enjoy the experience of everyone looking to see who's in the driving seat. That's quite a lot, isn't it? But don't skimp here. This is what defines the quality of the response. Now we're onto E for examples. This bit is optional. If you want to give ChatGPT some better direction, it might be worth adding some examples, maybe some examples of previous headlines or an example of the tone of voice. ChatGPT is great at learning from these and delivering a response in a similar style. Next, we've got A for adjustments. This is when you've tried a prompt and it's not quite giving you exactly what you want as a response. These are the refinements. For me they're often phrases like, don't use bullet points, or use subheads to break up the text. This is where the noodling happens to get things just right. And now we've got T for type of output. Tell ChatGPT exactly how you want it to format its response. An example might be, deliver your response as a 500-word article with a headline and a conclusion. The type of output can include articles, bullet points, tables, poems, jokes, scripts, just about anything that can be written. And finally, we've got E for extras. These are the little additions that can really make a big difference. I'm going to give you three examples here. The first is ignore everything before this prompt. This is useful if you don't want to draw on any of the conversation that you've had up until this point. I will often use this at the beginning of a prompt when I'm changing tack. Next we've got, ask me questions before you answer. Ugh, I often use a variant on this when I know that the AI needs lots of information to get its response right. We'll then have a conversation where I answer lots of questions and when ChatGPT thinks it's got enough information, it will then deliver a response. This one was an absolute game-changer for me. And finally, we've got, explain your thinking. It's a simple one. It's the equivalent of showing you're working in maths class. It's a way of verifying an answer by making sure the journey to that answer makes sense. This one is super useful. If you follow the CREATE formula, you're going to start writing prompts that are far more effective. I admit that they will take a bit of time to write but the extra effort pays off hugely in the results. (keys typing)

Download courses and learn on the go

Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.

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How to write an effective prompt for AI - How to Research and Write Using Generative AI Tools Video Tutorial | LinkedIn Learning, formerly Lynda.com

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All AI tools need the right information and instructions to deliver the best results. After watching this video, you will be able to create prompts for an AI.

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Useful primer on AI and GPT use and implications.

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GOV.UK (2023) Ethical guidance for AI use

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GOV.UK (2023) Ethical guidance for AI use

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Generative artificial intelligence (AI) in education

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The Department for Education’s (DfE) position on the use of generative AI in the education sector.

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Free tool allowing you to paster in paragraphs and Wordtune will check and suggest alternative versions and enable you to add inspirational quotes, further examples and more via the add 'Spice' tool.
Paid upgrade to enter more text in 1 pass.

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Creates multi-choice, gap fill, True/False for different ages from a prompt. Users can launch quiz online of download printable question and answer sheets.

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Fast and easy to use tools for creating quizzes and formative assessments in a flash.

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Creates documents, presentations, logos, infogrphics and more. AI tools allow images, presentation and text generation from prompts with auto-translate into other languages.

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Canva is a free-to-use online graphic design tool. Use it to create social media posts, presentations, posters, videos, logos and more.

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Summarises academic papers searching over 175 million works including some behind paywalls.
You can also run Elicit over papers saved as pdfs to automatically extract key information, analyse claims, and find similar works.

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Quillbot: summariser, plaigarism and grammar checker, citation creator

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Quillbot: summariser, plaigarism and grammar checker, citation creator

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The free version has a limit of 1500 words text each time for summaries and checking.

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Free version allows 250 characters per downloadable audio clip generation and a maximum of 5,000 characters per month

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Apologies for the technical glitch in our contact form. Emails sent using contact form between May 14 and May 24 (Span of last 10-days) were not received.
If you sent any emails during this time, kindly resend them to ensure that we receive your communication. Thanks for understanding.

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Apologies for the technical glitch in our contact form. Emails sent using contact form between May 14 and May 24 (Span of last 10-days) were not received.If you sent any emails during this time, kindly resend them to ensure that we receive your communication. Thanks for understanding.

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The PAIR framework for critically using and evaluating AI tools

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The PAIR framework for critically using and evaluating AI tools

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In the previous step, you discussed how teachers are using generative AI tools. In this step, you will discuss a framework that is designed to proactively integrate AI into our curriculums.

Generative AI is poised to create transformative changes, impacting not only the educational sector but also the wider professional world. In the face of these advancements, a purely defensive approach, centred on detecting and reducing AI usage, does not sufficiently equip our students for an AI-driven future.

The PAIR (Problem, AI, Interaction, Reflection) framework emerges from a motivation to proactively integrate AI into our curriculums. Specifically, it aims to cultivate five essential skills that are paramount for leveraging these technologies responsibly: problem formulation, exploration, experimentation, critical thinking and reflection (for a deeper insight into the importance of these skills, and details of the framework, please refer to my article, Are your students ready for AI?. The ultimate objective is to ensure that students can utilise generative AI tools effectively and ethically.

Key components of the framework

I developed the PAIR framework with simplicity, customisability and compatibility in mind, allowing it to be seamlessly adapted across various disciplines. The three key tenets of the framework are:

Human-centric: viewing AI as a tool to augment, not replace, human insight, judgment and creativity.

Skill-centric: focusing on the development of transferable AI skills over mastering specific tools.

Responsibility-centric: promoting effective and responsible use of generative AI tools.

The PAIR process is encapsulated in four steps:

Problem formulation: students define the problem or challenge they aim to address.​

AI tool selection: students identify and choose the most suitable generative AI tools for their needs. This involves exploring, comparing and evaluating the features of various generative AI options.

Interaction: students deploy the selected generative AI tools to tackle their problem, experimenting with diverse inputs and critically evaluating the outputs.

Reflection: students assess and report their experiences with the generative AI tools.

The table below provides a practical guide for applying the PAIR framework:

1. Problem

Formulate the problem

Identify the core problem, its components and constraints

2. AI

Select the suitable tools

Explore and identify the most suitable AI tools for your problem

3. Interaction

Interact with AI tools

Experiment with different ways to interact; critically evaluate outputs and integrate them to tackle the problem

4. Reflection

Reflect on the experience

Evaluate how the AI tool helped or hindered problem-solving; reflect on your feelings when collaborating with AI

Implementing the PAIR framework

Below are some ways that PAIR can be tailored to suit different academic objectives and contexts.

The PAIR framework can be seamlessly woven into existing coursework or serve as a foundation for the design of a new module. For the former application, module leaders could simply invite students to tackle the whole task or challenge highlighted in the assignment, or parts of it, using the PAIR framework. It is worth noting that 'problem' is broadly interpreted as a task or challenge integral to the assignment. This encompasses, but is not limited to, diverse tasks such as analysis or comparison (for instance, of legislation, data, code, case studies, theories, movements or ecosystems) and creation or design tasks (like developing algorithms, artworks, models, business plans, research protocols, simulations or chemical compounds).

Each component within the framework can be adjusted based on learning goals, available resources and the specific needs and attributes of the student body to ensure the framework complements the learning environment and supports the achievement of learning objectives.

1. Problem formulation

This is the foundation of PAIR. Approaches to this can vary, such as:

Open inquiry: students are granted autonomy to identify and craft their problem statement.

Closed inquiry: a structured format where students are given a predefined problem.

Semi-open/guided inquiries: module leaders can offer broad themes or ill-defined problems, letting students focus on specifics.

2. AI tool selection

This can also be tailored per the module's needs. Module leaders might, for example, suggest a list of freely accessible foundational models, such as Bard, Bing AI, ChatGPT 3.5, or Claude 2, in alignment with the learning objectives. In addition, or alternatively, students might be encouraged to explore and select AI tools based on their research. If the second option is preferred, it is important to ensure all students can access the recommended AI tools and recommend them to solely rely on free tools (or use those offering free trial versions).

3. Interaction

This stage encourages students to actively engage with their chosen AI tools. Emphasis can be placed on experimenting with different techniques to understand the tools' capabilities, or on the critical evaluation of results. Depending on learning objectives, module leaders may guide students to delve deeper into understanding advanced prompting strategies, feedback mechanisms and the nuances of AI responses. Alternatively, the focus might shift to scrutinising the quality, potential biases and underlying logic behind AI-generated outputs.

4. Reflection

This phase encourages students to introspectively analyse their experiences with the AI tools. Module leaders can provide avenues for students to contemplate the broader implications and intricacies of AI outputs. Topics of reflection might encompass AI's societal effects, its influence on specific disciplines, professions or tasks as well as their identity. Aligning these reflective prompts with course objectives encourages students to better contextualise their learnings and the potential ramifications of AI in the wider world.

In adapting the PAIR framework to coursework, it is important to tailor the complexity of the assignment (eg whether an inquiry is open versus closed) based on student characteristics such as their prior knowledge and cognitive abilities. It is also essential for module leaders to develop an understanding of generative AI tools.
Now that you have completed this step, you have developed an understanding of how you can proactively integrate generative AI in your module design. In the next step, you will learn about chatbots and study assistants.
References

Acar OA. Are Your Students Ready for AI?: A Four-Step Framework to Prepare Learners for a ChatGPT World [Internet]. Harvard Business Publishing Education; 2023 Jun 15.

Join the conversation

Considering the 'Reflection' component of the PAIR framework, how essential do you deem the process of introspection and contemplation after engaging with generative AI tools? Can taking a step back and reflecting alter the way we understand and harness AI? Share a specific moment or learning experience where reflection reshaped your perspective on AI or its applications.

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The PAIR framework for using GenAI in education

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In this article, Professor Oguz Acar demonstrates how to use the PAIR framework to integrate AI into your teaching to help students use GenAI better.

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Free translator to and from 150 modern languages capable of translating typed text, website links, images with text or uploaded documents.

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Provides definitions, synonyms, antonyms and example sentences for entered words for free.

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GPTionary: The AI-powered thesaurus and dictionary that leverages GPT and open-source language models to enhance vocabulary acquisition through concise word and phrase descriptions. Ideal for students, teachers, and lifelong learners looking to expand their knowledge base.

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Free session planner - enter topic or outcomes and Curipod creates a PowerPoint style presentation slide deck with embedded quiz questions, discussions and tasks all using active learning strategies.

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Generate lessons with AI

Plan and deliver interactive lessons on any topic - with help from AI

I am teacher of 15 years who, until recently, has delved no more deeply into tech in the classroom, and here I am encouraging colleagues to come on in and see Curipod in action and leading CPD sessions. To top it off, I'm spending my evenings playing on my iPad to see how many new lessons I can create, and what new features have been added that week. The time saved, and the improvement in my lesson materials, is tangible.
- Read the full case study with Aileen Wallace

Curious?

Join our online workshop to find out how Curipod can work for you and your students!

Book your spot

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Join the growing movement of teachers using Curipod to make interactive lessons filled with creativity, reflection and critical thinking

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Copy in a text block, type or upload a pdf or Office file to get a translation from or to 30 modern foreign languages.

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DeepL Translate: The world's most accurate translator

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Translate texts & full document files instantly. Accurate translations for individuals and Teams. Millions translate with DeepL every day.

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Otter.ai - Voice Meeting Notes & Real-time Transcription

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Auto-transcription from meeting audio on Zoom, Teams, Google meets - 30 minutes duration of meeting is free to download.

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Otter.ai - AI Meeting Note Taker & Real-time AI Transcription

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Otter.ai uses an AI Meeting Assistant to transcribe meetings in real time, record audio, capture slides, extract action items, and generate an AI meeting summary.

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Drop a PDF to get questions arising that a reader might have and then add these to the query for a summary

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ChatPDF is the fast and easy way to chat with any PDF, free and without sign-in. Talk to books, research papers, manuals, essays, legal contracts, whatever you have! The intelligence revolution is here, ChatGPT was just the beginning!

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Rowan Cheung @rowancheung - therundown.ai

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Rowan Cheung @rowancheung - therundown.ai

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Tweets by rowancheung

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Scribblediffusion.com - turns sketches to graphics

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Scribblediffusion.com - turns sketches to graphics

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Free Open Source platform allowing users to hand draw an image and generate a high quality graphic design based on it.

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Turn your sketch into a refined image using AI

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Padlet's 'I Can't Draw' graphics creator

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Padlet's 'I Can't Draw' graphics creator

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This image is the result of me using Padlet's 'I can't draw' AI-powered feature (click on the (...) to access this)
I asked Padlet to create a graphic design of AI represented in human form

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Free version showing adverts which generates a range of image types from a prompt. Pay versions work quicker and are add free.

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Use a camera to take a photo of a maths problem and Photomath will solve it showing the steps.
Free to use core tool with price options for video and animations.

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Solve math problems of any complexity with Photomath, the top-rated math camera solver app. Download now and ace math homework step-by-step.

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Creates documents, presentations and websites from your prompt. These are then editable via further prompts/image replacement.
Free ATM including downloading of PDF and web sharing / user collaboration

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Gamma | Best AI Presentation Maker & Website Builder

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Design stunning presentations, websites, and more with Gamma—your all-in-one AI-powered design partner. No code or design experience needed.

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101 Creative ideas to use AI in education

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101 Creative ideas to use AI in education

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This open crowdsourced collection by #creativeHE presents a rich tapestry of our collective thinking in the first months of 2023 stitching together potential alternative uses and applications of Artificial Intelligence (AI) that could make a difference and create new learning, development, teaching and assessment opportunities.

Experimentation is at the heart of learning, teaching and scholarship. Being open to diverse ideas will help us make novel connections that can lead to new discoveries and insights to make a positive contribution to our world. Ideas shared may be in its embryonic stage, but worth exploring further through active and creative inquiry.

We would like to illuminate the importance of responsible, critical and ethical use of AI in education settings and more generally.

We are grateful for all 101 contributions from 19 countries: Australia, Canada, China, Egypt, Germany, Greece, India, Israel, Italy, Ireland, Jordan, Liberia, Mexico, South Africa, Spain, Thailand, Turkey, United Kingdom and the US.

A special thank you to Bushra Hashim for the beautiful design.

Suggested citation: Nerantzi, C., Abegglen, S., Karatsiori, M. and Martinez-Arboleda, A. (Eds.) (2023). 101 Creative ideas to use AI in education. A collection curated by #creativeHE. Graphic Design by Bushra Hashim. CC-BY-NC-SA 4.0.

As the collection is made available under the Creative Commons License CC-BY-NC-SA license, anybody can use the collection as open data to further interrogate the use of AI in Education. Please share any resulting outcomes with the editorial team and the wider community.

The editors

"This collection represents vision; it embodies creativity. The importance of perspective and community of practice comes to life here in the breadth of examples demonstrating creative ideas to use AI in education. As we explore how we design new experiences for our learners and differentiate opportunities to engage in new ways, we have an opportunity to push our own boundaries and explore. We can collaborate, radically. This is a collection that will only grow as we shift our own practice and as we allow ourselves to experiment and iterate for a transformational student experience." Dr Margaret Korosec, Dean of Online and Digital Education, University of Leeds.

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101 creative ideas to use AI in education, A crowdsourced collection

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This open crowdsourced collection by #creativeHE presents a rich tapestry of our collective thinking in the first months of 2023 stitching together potential alternative uses and applications of Artificial Intelligence (AI) that could make a difference and create new learning, development, teaching and assessment opportunities. Experimentation is at the heart of learning, teaching and scholarship.

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GenAI for Educators (6 hours or 2 x half days)

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GenAI for Educators (6 hours or 2 x half days)

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Key Learning Areas:

By the end of the course all delegates will be able to:

Describe the purpose of a range of genAI tools for business including ChatGPT, Microsoft Co-Pilot, Microsoft Translate

Evaluate the likely benefits and costs of using different versions of genAI and how up-to-date they are

Use prompt engineering techniques to create a range of learning and learning support resources relevant for own organisation

Review examples of AI / digital pedagogy in training and development scenarios

Evaluate some of the disadvantages of genAI use including ethical and data protection issues and the risk of ‘hallucinated’ responses

Describe likely future developments in genAI

Formulate an action plan for own genAI use development and organisational development and policy

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Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

Learn more about how sentiment analysis works, its challenges, and how you can use sentiment analysis to improve processes, decision-making, customer satisfaction and more.

Once you're familiar with the basics, get started with easy-to-use sentiment analysis tools that are ready to use right off the bat.

1. What Is Sentiment Analysis?

2. Types of Sentiment Analysis

3. Why Is Sentiment Analysis Important?

4. Sentiment Analysis Examples & Break Down of Trustpilot Reviews

5. How Does Sentiment Analysis Work?

6. Sentiment Analysis Challenges

7. Sentiment Analysis Applications

8. Sentiment Analysis Tools & Tutorials

9. Sentiment Analysis Research & Courses

What Is Sentiment Analysis?

Sentiment analysis is the process of detecting positive or negative sentiment in text. It's often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers.

Types of Sentiment Analysis

Sentiment analysis focuses on the polarity of a text ( positive, negative, neutral) but it also goes beyond polarity to detect specific feelings and emotions ( angry, happy, sad, etc), urgency ( urgent, not urgent) and even intentions ( interested v. not interested).

Depending on how you want to interpret customer feedback and queries, you can define and tailor your categories to meet your sentiment analysis needs. In the meantime, here are some of the most popular types of sentiment analysis:

Graded Sentiment Analysis

If polarity precision is important to your business, you might consider expanding your polarity categories to include different levels of positive and negative:

This is usually referred to as graded or fine-grained sentiment analysis, and could be used to interpret 5-star ratings in a review, for example:

Very Positive = 5 stars

Very Negative = 1 star

Emotion detection

Emotion detection sentiment analysis allows you to go beyond polarity to detect emotions, like happiness, frustration, anger, and sadness.

Many emotion detection systems use lexicons (i.e. lists of words and the emotions they convey) or complex machine learning algorithms.

One of the downsides of using lexicons is that people express emotions in different ways. Some words that typically express anger, like bad or kill (e.g. your product is so bad or your customer support is killing me) might also express happiness (e.g. this is bad ass or you are killing it).

Aspect-based Sentiment Analysis

Usually, when analyzing sentiments of texts you'll want to know which particular aspects or features people are mentioning in a positive, neutral, or negative way.

That's where aspect-based sentiment analysis can help, for example in this product review: "The battery life of this camera is too short", an aspect-based classifier would be able to determine that the sentence expresses a negative opinion about the battery life of the product in question.

Multilingual sentiment analysis

Multilingual sentiment analysis can be difficult. It involves a lot of preprocessing and resources. Most of these resources are available online (e.g. sentiment lexicons), while others need to be created (e.g. translated corpora or noise detection algorithms), but you'll need to know how to code to use them.

Alternatively, you could detect language in texts automatically with a language classifier, then train a custom sentiment analysis model to classify texts in the language of your choice.

Why Is Sentiment Analysis Important?

Since humans express their thoughts and feelings more openly than ever before, sentiment analysis is fast becoming an essential tool to monitor and understand sentiment in all types of data.

Automatically analyzing customer feedback, such as opinions in survey responses and social media conversations, allows brands to learn what makes customers happy or frustrated, so that they can tailor products and services to meet their customers' needs.

For example, using sentiment analysis to automatically analyze 4,000+ open-ended responses in your customer satisfaction surveys could help you discover why customers are happy or unhappy at each stage of the customer journey.

Maybe you want to track brand sentiment so you can detect disgruntled customers immediately and respond as soon as possible. Maybe you want to compare sentiment from one quarter to the next to see if you need to take action. Then you could dig deeper into your qualitative data to see why sentiment is falling or rising.

The overall benefits of sentiment analysis include:

Can you imagine manually sorting through thousands of tweets, customer support conversations, or surveys? There's just too much business data to process manually. Sentiment analysis helps businesses process huge amounts of unstructured data in an efficient and cost-effective way.

Sentiment analysis can identify critical issues in real-time, for example is a PR crisis on social media escalating? Is an angry customer about to churn? Sentiment analysis models can help you immediately identify these kinds of situations, so you can take action right away.

It's estimated that people only agree around 60-65% of the time when determining the sentiment of a particular text. Tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs.

By using a centralized sentiment analysis system, companies can apply the same criteria to all of their data, helping them improve accuracy and gain better insights.

The applications of sentiment analysis are endless. So, to help you understand how sentiment analysis could benefit your business, let's take a look at some examples of texts that you could analyze using sentiment analysis.

Then, we'll jump into a real-world example of how Chewy, a pet supplies company, was able to gain a much more nuanced (and useful!) understanding of their reviews through the application of sentiment analysis.

Sentiment Analysis Examples

To understand the goal and challenges of sentiment analysis, here are some examples:
Basic examples of sentiment analysis data

Netflix has the best selection of films

Hulu has a great UI

I dislike like the new crime series

I hate waiting for the next series to come out

More challenging examples of sentiment analysis

I do not dislike horror movies. (phrase with negation)

Disliking horror movies is not uncommon. (negation, inverted word order)

Sometimes I really hate the show. (adverbial modifies the sentiment)

I love having to wait two months for the next series to come out! ( sarcasm)

The final episode was surprising with a terrible twist at the end (negative term used in a positive way)

The film was easy to watch but I would not recommend it to my friends. (difficult to categorize)

I LOL'd at the end of the cake scene (often hard to understand new terms)

Now, let's take a look at some real reviews on Trustpilot and see how MonkeyLearn's sentiment analysis tools fare when it comes to recognizing and categorizing sentiment.

Case Study: Sentiment analysis on TrustPilot Reviews

Chewy is a pet supplies company - an industry with no shortage of competition, so providing a superior customer experience (CX) to their customers can be a massive difference maker.

For this reason, online reviews can be an extremely valuable source of information to gain customer insights to improve their CX. Chewy has thousands of reviews in TrustPilot, this is what their review archive looks like:

It is easy to draw a general conclusion about Chewy's relative success from this alone - 82% of responses being excellent is a great starting place.

But TrustPilot's results alone fall short if Chewy's goal is to improve its services. This perfunctory overview fails to provide actionable insight, the cornerstone, and end goal, of effective sentiment analysis.

If Chewy wanted to unpack the what and why behind their reviews, in order to further improve their services, they would need to analyze each and every negative review at a granular level.

But with sentiment analysis tools, Chewy could plug in their 5,639 (at the time) TrustPilot reviews to gain instant sentiment analysis insights.

We uploaded and analyzed Chewy's reviews to MonkeyLearn's all-in-one data analysis and visualization studio to generate the following dashboard:

Feel free to click this link to peruse the results at your leisure - as this sample dashboard is a public demo, you can click through and explore the inputs and filters at work yourself.

While there is a ton more to explore, in this breakdown we are going to focus on four sentiment analysis data visualization results that the dashboard has visualized for us.

1. Overall sentiment

We'll begin by pulling the relevant graphic from the above dashboard.

You'll notice that these results are very different from TrustPilot's overview (82% excellent, etc). This is because MonkeyLearn's sentiment analysis AI performs advanced sentiment analysis, parsing through each review sentence by sentence, word by word.

What you are left with is an accurate assessment of everything customers have written, rather than a simple tabulation of stars. This analysis can point you towards friction points much more accurately and in much more detail.

Read up on the mechanics of how sentiment analysis works below.

2. Sentiment over time

Here's our handy-dandy sentiment over time graph, blown up:

This data visualization sample is classic temporal datavis, a datavis type that tracks results and plots them over a period of time.

This graph expands on our Overall Sentiment data - it tracks the overall proportion of positive, neutral, and negative sentiment in the reviews from 2016 to 2021.

This graph informs the gradual change in the content of their written reviews over this five year period. For instance, negative responses went down from 2019-2020, then jumped back up to previous levels in 2021.

3. Sentiment by rating

Now we jump to something that anchors our text-based sentiment to TrustPilot's earlier results.

By taking each TrustPilot category from 1-Bad to 5-Excellent, and breaking down the text of the written reviews from the scores you can derive the above graphic.

Looking at the results, and courtesy of taking a deeper look at the reviews via sentiment analysis, we can draw a couple interesting conclusions right off the bat.

1. TrustPilots results aren't useless - the better reviews have higher proportions of positive sentiment and the worse reviews have more negative sentiment. But, all reviews contain a little bit of all types of sentiment - we've learned that our reviews are nuanced and thus likely have even more hidden insight for us!

2. Our reviews are polarized. They skew in amounts towards 5 and 1.

These quick takeaways point us towards goldmines for future analysis. Namely, the positive sentiment sections of negative reviews and the negative section of positive ones, and the 2 - 4 reviews (why do they feel the way they do, how could we improve their scores?).

4. Sentiment by Topic

Finally, we can take a look at Sentiment by Topic to begin to illustrate how sentiment analysis can take us even further into our data.

The above chart applies product-linked text classification in addition to sentiment analysis to pair given sentiment to product/service specific features, this is known as aspect-based sentiment analysis.

This means we can know how our customers feel about what, helping us zero in and fix specific pain points or issues.

These are all great jumping off points designed to visually demonstrate the value of sentiment analysis - but they only scratch the surface of its true power.

Read on for a step-by-step walkthrough of how sentiment analysis works.

How Does Sentiment Analysis Work?

Sentiment analysis, otherwise known as opinion mining, works thanks to natural language processing (NLP) and machine learning algorithms, to automatically determine the emotional tone behind online conversations.

There are different algorithms you can implement in sentiment analysis models, depending on how much data you need to analyze, and how accurate you need your model to be. We'll go over some of these in more detail, below.
Sentiment analysis algorithms fall into one of three buckets:

Rule-based: these systems automatically perform sentiment analysis based on a set of manually crafted rules.

Automatic: systems rely on machine learning techniques to learn from data.

Hybrid systems combine both rule-based and automatic approaches.

Rule-based Approaches

Usually, a rule-based system uses a set of human-crafted rules to help identify subjectivity, polarity, or the subject of an opinion.

These rules may include various NLP techniques developed in computational linguistics, such as:

Stemming, tokenization, part-of-speech tagging and parsing.

Lexicons (i.e. lists of words and expressions).

Here's a basic example of how a rule-based system works:

1. Defines two lists of polarized words (e.g. negative words such as bad, worst, ugly, etc and positive words such as good, best, beautiful, etc).

2. Counts the number of positive and negative words that appear in a given text.

3. If the number of positive word appearances is greater than the number of negative word appearances, the system returns a positive sentiment, and vice versa. If the numbers are even, the system will return a neutral sentiment.

Rule-based systems are very naive since they don't take into account how words are combined in a sequence. Of course, more advanced processing techniques can be used, and new rules added to support new expressions and vocabulary. However, adding new rules may affect previous results, and the whole system can get very complex. Since rule-based systems often require fine-tuning and maintenance, they'll also need regular investments.

Automatic Approaches

Automatic methods, contrary to rule-based systems, don't rely on manually crafted rules, but on machine learning techniques. A sentiment analysis task is usually modeled as a classification problem, whereby a classifier is fed a text and returns a category, e.g. positive, negative, or neutral.

Here's how a machine learning classifier can be implemented:

The Training and Prediction Processes

In the training process (a), our model learns to associate a particular input (i.e. a text) to the corresponding output (tag) based on the test samples used for training. The feature extractor transfers the text input into a feature vector. Pairs of feature vectors and tags (e.g. positive, negative, or neutral) are fed into the machine learning algorithm to generate a model.

In the prediction process (b), the feature extractor is used to transform unseen text inputs into feature vectors. These feature vectors are then fed into the model, which generates predicted tags (again, positive, negative, or neutral).

Feature Extraction from Text

The first step in a machine learning text classifier is to transform the text extraction or text vectorization, and the classical approach has been bag-of-words or bag-of-ngrams with their frequency.

More recently, new feature extraction techniques have been applied based on word embeddings (also known as word vectors). This kind of representations makes it possible for words with similar meaning to have a similar representation, which can improve the performance of classifiers.

Classification Algorithms

The classification step usually involves a statistical model like Naïve Bayes, Logistic Regression, Support Vector Machines, or Neural Networks:

Naïve Bayes: a family of probabilistic algorithms that uses Bayes's Theorem to predict the category of a text.

Linear Regression: a very well-known algorithm in statistics used to predict some value (Y) given a set of features (X).

Support Vector Machines: a non-probabilistic model which uses a representation of text examples as points in a multidimensional space. Examples of different categories (sentiments) are mapped to distinct regions within that space. Then, new texts are assigned a category based on similarities with existing texts and the regions they're mapped to.

Deep Learning: a diverse set of algorithms that attempt to mimic the human brain, by employing artificial neural networks to process data.

Hybrid Approaches

Hybrid systems combine the desirable elements of rule-based and automatic techniques into one system. One huge benefit of these systems is that results are often more accurate.

Sentiment Analysis Challenges

Sentiment analysis is one of the hardest tasks in natural language processing because even humans struggle to analyze sentiments accurately.

Data scientists are getting better at creating more accurate sentiment classifiers, but there's still a long way to go. Let's take a closer look at some of the main challenges of machine-based sentiment analysis:

Subjectivity and Tone

There are two types of text: subjective and objective. Objective texts do not contain explicit sentiments, whereas subjective texts do. Say, for example, you intend to analyze the sentiment of the following two texts:
The package is nice. The package is red.
Most people would say that sentiment is positive for the first one and neutral for the second one, right? All predicates (adjectives, verbs, and some nouns) should not be treated the same with respect to how they create sentiment. In the examples above, nice is more subjective than red.

Context and Polarity

All utterances are uttered at some point in time, in some place, by and to some people, you get the point. All utterances are uttered in context. Analyzing sentiment without context gets pretty difficult. However, machines cannot learn about contexts if they are not mentioned explicitly. One of the problems that arise from context is changes in polarity. Look at the following responses to a survey:
Everything about it. Absolutely nothing!
Imagine the responses above come from answers to the question What did you like about the event? The first response would be positive and the second one would be negative, right? Now, imagine the responses come from answers to the question What did you DISlike about the event? The negative in the question will make sentiment analysis change altogether.

A good deal of preprocessing or postprocessing will be needed if we are to take into account at least part of the context in which texts were produced. However, how to preprocess or postprocess data in order to capture the bits of context that will help analyze sentiment is not straightforward.

Irony and Sarcasm

When it comes to irony and sarcasm, people express their negative sentiments using positive words, which can be difficult for machines to detect without having a thorough understanding of the context of the situation in which a feeling was expressed.

For example, look at some possible answers to the question, Did you enjoy your shopping experience with us?
Yeah, sure. So smooth! Not one, but many!
What sentiment would you assign to the responses above? The first response with an exclamation mark could be negative, right? The problem is there is no textual cue that will help a machine learn, or at least question that sentiment since yeah and sure often belong to positive or neutral texts.

How about the second response? In this context, sentiment is positive, but we're sure you can come up with many different contexts in which the same response can express negative sentiment.

Comparisons

How to treat comparisons in sentiment analysis is another challenge worth tackling. Look at the texts below:
This product is second to none. This is better than older tools. This is better than nothing.
The first comparison doesn't need any contextual clues to be classified correctly. It's clear that it's positive.

The second and third texts are a little more difficult to classify, though. Would you classify them as neutral, positive, or even negative? Once again, context can make a difference. For example, if the 'older tools' in the second text were considered useless, then the second text is pretty similar to the third text.

Emojis

There are two types of emojis according to Guibon et al.. Western emojis (e.g. :D) are encoded in only one or two characters, whereas Eastern emojis (e.g. ¯ \ (ツ) / ¯) are a longer combination of characters of a vertical nature. Emojis play an important role in the sentiment of texts, particularly in tweets.

You'll need to pay special attention to character-level, as well as word-level, when performing sentiment analysis on tweets. A lot of preprocessing might also be needed. For example, you might want to preprocess social media content and transform both Western and Eastern emojis into tokens and whitelist them (i.e. always take them as a feature for classification purposes) in order to help improve sentiment analysis performance.

Here's a quite comprehensive list of emojis and their unicode characters that may come in handy when preprocessing.

Defining Neutral

Defining what we mean by neutral is another challenge to tackle in order to perform accurate sentiment analysis. As in all classification problems, defining your categories -and, in this case, the neutral tag- is one of the most important parts of the problem. What you mean by neutral, positive, or negative does matter when you train sentiment analysis models. Since tagging data requires that tagging criteria be consistent, a good definition of the problem is a must.

Here are some ideas to help you identify and define neutral texts:

1. Objective texts. So called objective texts do not contain explicit sentiments, so you should include those texts into the neutral category.

2. Irrelevant information. If you haven't preprocessed your data to filter out irrelevant information, you can tag it neutral. However, be careful! Only do this if you know how this could affect overall performance. Sometimes, you will be adding noise to your classifier and performance could get worse.

3. Texts containing wishes. Some wishes like, I wish the product had more integrations are generally neutral. However, those including comparisons like, I wish the product were better are pretty difficult to categorize

Human Annotator Accuracy

Sentiment analysis is a tremendously difficult task even for humans. On average, inter-annotator agreement (a measure of how well two (or more) human labelers can make the same annotation decision) is pretty low when it comes to sentiment analysis. And since machines learn from labeled data, sentiment analysis classifiers might not be as precise as other types of classifiers.

Still, sentiment analysis is worth the effort, even if your sentiment analysis predictions are wrong from time to time. By using MonkeyLearn's sentiment analysis model, you can expect correct predictions about 70-80% of the time you submit your texts for classification.

If you are new to sentiment analysis, then you'll quickly notice improvements. For typical use cases, such as ticket routing, brand monitoring, and VoC analysis, you'll save a lot of time and money on tedious manual tasks.

Sentiment Analysis Use Cases & Applications

The applications of sentiment analysis are endless and can be applied to any industry, from finance and retail to hospitality and technology. Below, we've listed some of the most popular ways that sentiment analysis is being used in business:

On the fateful evening of April 9th, 2017, United Airlines forcibly removed a passenger from an overbooked flight. The nightmare-ish incident was filmed by other passengers on their smartphones and posted immediately. One of the videos, posted to Facebook, was shared more than 87,000 times and viewed 6.8 million times by 6pm on Monday, just 24 hours later.

The fiasco was only magnified by the company's dismissive response. On Monday afternoon, United's CEO tweeted a statement apologizing for "having to re-accommodate customers."

This is exactly the kind of PR catastrophe you can avoid with sentiment analysis. It's an example of why it's important to care, not only about if people are talking about your brand, but how they're talking about it. More mentions don't equal positive mentions.

Brands of all shapes and sizes have meaningful interactions with customers, leads, even their competition, all across social media. By monitoring these conversations you can understand customer sentiment in real time and over time, so you can detect disgruntled customers immediately and respond as soon as possible.

Most marketing departments are already tuned into online mentions as far as volume - they measure more chatter as more brand awareness. But businesses need to look beyond the numbers for deeper insights.

Brand Monitoring

Not only do brands have a wealth of information available on social media, but across the internet, on news sites, blogs, forums, product reviews, and more. Again, we can look at not just the volume of mentions, but the individual and overall quality of those mentions.

In our United Airlines example, for instance, the flare-up started on the social media accounts of just a few passengers. Within hours, it was picked up by news sites and spread like wildfire across the US, then to China and Vietnam, as United was accused of racial profiling against a passenger of Chinese-Vietnamese descent. In China, the incident became the number one trending topic on Weibo, a microblogging site with almost 500 million users.

And again, this is all happening within mere hours of the incident.

Brand monitoring offers a wealth of insights from conversations happening about your brand from all over the internet. Analyze news articles, blogs, forums, and more to gauge brand sentiment, and target certain demographics or regions, as desired. Automatically categorize the urgency of all brand mentions and route them instantly to designated team members.

Get an understanding of customer feelings and opinions, beyond mere numbers and statistics. Understand how your brand image evolves over time, and compare it to that of your competition. You can tune into a specific point in time to follow product releases, marketing campaigns, IPO filings, etc., and compare them to past events.

Real-time sentiment analysis allows you to identify potential PR crises and take immediate action before they become serious issues. Or identify positive comments and respond directly, to use them to your benefit.

Example: Expedia Canada

Around Christmas time, Expedia Canada ran a classic "escape winter" marketing campaign. All was well, except for the screeching violin they chose as background music. Understandably, people took to social media, blogs, and forums. Expedia noticed right away and removed the ad.

Then, they created a series of follow-up spin-off videos: one showed the original actor smashing the violin; another invited a real negative Twitter user to rip the violin out of the actor's hands on screen. Though their original campaign was a flop, Expedia were able to redeem themselves by listening to their customers and responding.

Sentiment analysis allows you to automatically monitor all chatter around your brand and detect and address this type of potentially-explosive scenario while you still have time to defuse it.

Voice of Customer (VoC)

Social media and brand monitoring offer us immediate, unfiltered, and invaluable information on customer sentiment, but you can also put this analysis to work on surveys and customer support interactions.

Net Promoter Score (NPS) surveys are one of the most popular ways for businesses to gain feedback with the simple question: Would you recommend this company, product, and/or service to a friend or family member? These result in a single score on a number scale.

Businesses use these scores to identify customers as promoters, passives, or detractors. The goal is to identify overall customer experience, and find ways to elevate all customers to "promoter" level, where they, theoretically, will buy more, stay longer, and refer other customers.

Numerical (quantitative) survey data is easily aggregated and assessed. But the next question in NPS surveys, asking why survey participants left the score they did, seeks open-ended responses, or qualitative data.

Open-ended survey responses were previously much more difficult to analyze, but with sentiment analysis these texts can be classified into positive and negative (and everywhere in between) offering further insights into the Voice of Customer (VoC).

Sentiment analysis can be used on any kind of survey - quantitative and qualitative - and on customer support interactions, to understand the emotions and opinions of your customers. Tracking customer sentiment over time adds depth to help understand why NPS scores or sentiment toward individual aspects of your business may have changed.

You can use it on incoming surveys and support tickets to detect customers who are 'strongly negative' and target them immediately to improve their service. Zero in on certain demographics to understand what works best and how you can improve.

Real-time analysis allows you to see shifts in VoC right away and understand the nuances of the customer experience over time beyond statistics and percentages.

Discover how we analyzed the sentiment of thousands of Facebook reviews, and transformed them into actionable insights.

Example: McKinsey City Voices project

In Brazil, federal public spending rose by 156% from 2007 to 2015, while satisfaction with public services steadily decreased. Unhappy with this counterproductive progress, the Urban Planning Department recruited McKinsey to help them focus on user experience, or "citizen journeys," when delivering services. This citizen-centric style of governance has led to the rise of what we call Smart Cities.

McKinsey developed a tool called City Voices, which conducts citizen surveys across more than 150 metrics, and then runs sentiment analysis to help leaders understand how constituents live and what they need, in order to better inform public policy. By using this tool, the Brazilian government was able to uncover the most urgent needs - a safer bus system, for instance - and improve them first.

If this can be successful on a national scale, imagine what it can do for your company.

Customer Service

We already looked at how we can use sentiment analysis in terms of the broader VoC, so now we'll dial in on customer service teams.

We all know the drill: stellar customer experiences means a higher rate of returning customers. Leading companies know that how they deliver is just as, if not more, important as what they deliver. Customers expect their experience with companies to be immediate, intuitive, personal, and hassle-free. If not, they'll leave and do business elsewhere. Did you know that one in three customers will leave a brand after just one bad experience?

You can use sentiment analysis and text classification to automatically organize incoming support queries by topic and urgency to route them to the correct department and make sure the most urgent are handled right away.

Analyze customer support interactions to ensure your employees are following appropriate protocol. Increase efficiency, so customers aren't left waiting for support. Decrease churn rates; after all it's less hassle to keep customers than acquire new ones.

Discover how we analyzed customer support interactions on Twitter.

Market Research

Sentiment analysis empowers all kinds of market research and competitive analysis. Whether you're exploring a new market, anticipating future trends, or seeking an edge on the competition, sentiment analysis can make all the difference.

You can analyze online reviews of your products and compare them to your competition. Maybe your competitor released a new product that landed as a flop. Find out what aspects of the product performed most negatively and use it to your advantage.

Follow your brand and your competition in real time on social media. Locate new markets where your brand is likely to succeed. Uncover trends just as they emerge, or follow long-term market leanings through analysis of formal market reports and business journals.

You'll tap into new sources of information and be able to quantify otherwise qualitative information. With social data analysis you can fill in gaps where public data is scarce, like emerging markets.

Discover how to analyze the sentiment of hotel reviews on TripAdvisor or perform sentiment analysis on Yelp restaurant reviews.

Sentiment Analysis Tools & Tutorials

Sentiment analysis is a vast topic, and it can be intimidating to get started. Luckily, there are many useful resources, from helpful tutorials to all kinds of free online tools, to help you take your first steps.

Free Online Sentiment Analysis Tools

A good start to your journey is to simply play around with a sentiment analysis tool. A little first-hand experience will help you understand how it works

Next, to take your sentiment analysis further, you'll want to try out MonkeyLearn's sentiment analysis and keyword template. First, you'll need sign up, then walk through the following steps:
1. Choose Keyword + Sentiment Analysis template 2. Upload your data
​​If you don't have a CSV, you can use our sample dataset.
3. Match the CSV columns to the dashboard fields
In this template, there is only one field: text. If you have more than one column in your dataset, choose the column that has the text you would like to analyze.
4. Name your workflow 5. Wait for your data to import 6. Explore your dashboard!
You can:

Filter by sentiment or keyword.

Share via email with other coworkers.

Open Source vs SaaS (Software as a Service) Sentiment Analysis Tools

When it comes to sentiment analysis (and text analysis in general), you have two choices: build your own solution or buy a tool.

Open source libraries in languages like Python and Java are particularly well positioned to build your own sentiment analysis solution because their communities lean more heavily toward data science, like natural language processing and deep learning for sentiment analysis. But you'll need a team of data scientists and engineers on board, huge upfront investments, and time to spare.

SaaS tools offer the option to implement pre-trained sentiment analysis models immediately or custom-train your own, often in just a few steps. These tools are recommended if you don't have a data science or engineering team on board, since they can be implemented with little or no code and can save months of work and money (upwards of $100,000).

Another key advantage of SaaS tools is that you don't even need to know how to code; they provide integrations with third-party apps, like MonkeyLearn's Zendesk, Excel and Zapier Integrations.

If you want to get started with these out-of-the-box tools, check out this guide to the best SaaS tools for sentiment analysis, which also come with APIs for seamless integration with your existing tools.

Or start learning how to perform sentiment analysis using MonkeyLearn's API and the pre-built sentiment analysis model, with just six lines of code. Then, train your own custom sentiment analysis model using MonkeyLearn's easy-to-use UI.

If you're still convinced that you need to build your own sentiment analysis solution, check out these tools and tutorials in various programming languages:

Sentiment Analysis Python

Scikit-learn is the go-to library for machine learning and has useful tools for text vectorization. Training a classifier on top of vectorizations, like frequency or tf-idf text vectorizers is quite straightforward. Scikit-learn has implementations for Support Vector Machines, Naïve Bayes, and Logistic Regression, among others.

NLTK has been the traditional NLP library for Python. It has an active community and offers the possibility to train machine learning classifiers.

SpaCy is an NLP library with a growing community. Like NLTK, it provides a strong set of low-level functions for NLP and support for training text classifiers.

TensorFlow, developed by Google, provides a low-level set of tools to build and train neural networks. There's also support for text vectorization, both on traditional word frequency and on more advanced through-word embeddings.

Keras provides useful abstractions to work with multiple neural network types, like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and easily stack layers of neurons. Keras can be run on top of Tensorflow or Theano. It also provides useful tools for text classification.

PyTorch is a recent deep learning framework backed by some prestigious organizations like Facebook, Twitter, Nvidia, Salesforce, Stanford University, University of Oxford, and Uber. It has quickly developed a strong community.

Tutorials to try out:
Sentiment Analysis Javascript

Java is another programming language with a strong community around data science with remarkable data science libraries for NLP.

OpenNLP: a toolkit that supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution.

Stanford CoreNLP: a Java suite of core NLP tools provided by The Stanford NLP Group.

Lingpipe: a Java toolkit for processing text using computational linguistics. LingPipe is often used for text classification and entity extraction.

Weka: a set of tools created by The University of Waikato for data pre-processing, classification, regression, clustering, association rules, and visualization.

Sentiment Analysis Research & Courses

After learning the basics of sentiment analysis, and understanding how it can help you, you might want to delve further into the topic:

Sentiment Analysis Papers

The literature around sentiment analysis is massive; there are more than 55,700 scholarly articles, papers, theses, books, and abstracts out there.

The following are the most frequently cited and read papers in the sentiment analysis community in general:

Sentiment Analysis Books

Bing Liu is a thought leader in the field of machine learning and has written a book about sentiment analysis and opinion mining.

Useful for those starting research on sentiment analysis, Liu does a wonderful job of explaining sentiment analysis in a way that is highly technical, yet understandable. In the book, he covers different aspects of sentiment analysis including applications, research, sentiment classification using supervised and unsupervised learning, sentence subjectivity, aspect-based sentiment analysis, and more.

For those who want to learn about deep-learning based approaches for sentiment analysis, a relatively new and fast-growing research area, take a look at Deep-Learning Based Approaches for Sentiment Analysis.

Sentiment Analysis Courses and Lectures

Another good way to go deeper with sentiment analysis is mastering your knowledge and skills in natural language processing (NLP), the computer science field that focuses on understanding 'human' language.

By combining machine learning, computational linguistics, and computer science, NLP allows a machine to understand natural language including people's sentiments, evaluations, attitudes, and emotions from written language.

There are a large number of courses, lectures, and resources available online, but the essential NLP course is the Stanford Coursera course by Dan Jurafsky and Christopher Manning. By taking this course, you will get a step-by-step introduction to the field by two of the most reputable names in the NLP community.

If you want a more hands-on course, you should enroll in the Data Science: Natural Language Processing (NLP) in Python on Udemy. This course gives you a good introduction to NLP and what it can do, but it will also make you build different projects in Python, including a spam detector, a sentiment analyzer, and an article spinner. Most of the lectures are really short (~5 minutes) and the course strikes the right balance between practical and theoretical content.

Sentiment Analysis Datasets

The key part for mastering sentiment analysis is working on different datasets and experimenting with different approaches. First, you'll need to get your hands on data and procure a dataset which you will use to carry out your experiments.

The following are some of our favorite sentiment analysis datasets for experimenting with sentiment analysis and a machine learning approach. They're open and free to download:

Product reviews: this dataset consists of a few million Amazon customer reviews with star ratings, super useful for training a sentiment analysis model.

Restaurant reviews: this dataset consists of 5,2 million Yelp reviews with star ratings.

Movie reviews: this dataset consists of 1,000 positive and 1,000 negative processed reviews. It also provides 5,331 positive and 5,331 negative processed sentences / snippets.

Fine food reviews: this dataset consists of ~500,000 food reviews from Amazon. It includes product and user information, ratings, and a plain text version of every review.

Twitter airline sentiment on Kaggle: this dataset consists of ~15,000 labeled tweets (positive, neutral, and negative) about airlines.

First GOP Debate Twitter Sentiment: this dataset consists of ~14,000 labeled tweets (positive, neutral, and negative) about the first GOP debate in 2016.

If you are interested in rule-based approach, the following is a varied list of sentiment analysis lexicons that will come in handy. These lexicons provide a set of dictionaries of words with labels specifying their sentiments across different domains. The following lexicons are really useful to identify the sentiment of texts:

Parting words

Sentiment analysis can be applied to countless aspects of business, from brand monitoring and product analytics, to customer service and market research. By incorporating it into their existing systems and analytics, leading brands (not to mention entire cities) are able to work faster, with more accuracy, toward more useful ends.

Sentiment analysis has moved beyond merely an interesting, high-tech whim, and will soon become an indispensable tool for all companies of the modern age. Ultimately, sentiment analysis enables us to glean new insights, better understand our customers, and empower our own teams more effectively so that they do better and more productive work.

MonkeyLearn is an online platform that makes it easy to perform text analytics with machine learning and data visualization tools.

If you need help building a sentiment analysis system for your business, visit MonkeyLearn Studio and request a demo.

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Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

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Trained on Adobe Stock images, openly licensed content, and public domain content, Firefly is designed to be safe for commercial use. To ensure that creators can benefit from generative AI, we've developed a compensation model for Adobe Stock contributors whose content is used in the dataset to retrain Firefly models.

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Adobe Firefly - Generative AI for everyone

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Use simple text prompts in 100+ languages to experiment and create extraordinary images, text styles, color schemes, and more with the Adobe Firefly web app.

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The Bletchley Declaration (2023) on AI Safety

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The Bletchley Declaration (2023) on AI Safety

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Artificial Intelligence ( AI) presents enormous global opportunities: it has the potential to transform and enhance human wellbeing, peace and prosperity. To realise this, we affirm that, for the good of all, AI should be designed, developed, deployed, and used, in a manner that is safe, in such a way as to be human-centric, trustworthy and responsible. We welcome the international community's efforts so far to cooperate on AI to promote inclusive economic growth, sustainable development and innovation, to protect human rights and fundamental freedoms, and to foster public trust and confidence in AI systems to fully realise their potential.

AI systems are already deployed across many domains of daily life including housing, employment, transport, education, health, accessibility, and justice, and their use is likely to increase. We recognise that this is therefore a unique moment to act and affirm the need for the safe development of AI and for the transformative opportunities of AI to be used for good and for all, in an inclusive manner in our countries and globally. This includes for public services such as health and education, food security, in science, clean energy, biodiversity, and climate, to realise the enjoyment of human rights, and to strengthen efforts towards the achievement of the United Nations Sustainable Development Goals.

Alongside these opportunities, AI also poses significant risks, including in those domains of daily life. To that end, we welcome relevant international efforts to examine and address the potential impact of AI systems in existing fora and other relevant initiatives, and the recognition that the protection of human rights, transparency and explainability, fairness, accountability, regulation, safety, appropriate human oversight, ethics, bias mitigation, privacy and data protection needs to be addressed. We also note the potential for unforeseen risks stemming from the capability to manipulate content or generate deceptive content. All of these issues are critically important and we affirm the necessity and urgency of addressing them.

Particular safety risks arise at the 'frontier' of AI, understood as being those highly capable general-purpose AI models, including foundation models, that could perform a wide variety of tasks - as well as relevant specific narrow AI that could exhibit capabilities that cause harm - which match or exceed the capabilities present in today's most advanced models. Substantial risks may arise from potential intentional misuse or unintended issues of control relating to alignment with human intent. These issues are in part because those capabilities are not fully understood and are therefore hard to predict. We are especially concerned by such risks in domains such as cybersecurity and biotechnology, as well as where frontier AI systems may amplify risks such as disinformation. There is potential for serious, even catastrophic, harm, either deliberate or unintentional, stemming from the most significant capabilities of these AI models. Given the rapid and uncertain rate of change of AI, and in the context of the acceleration of investment in technology, we affirm that deepening our understanding of these potential risks and of actions to address them is especially urgent.

Many risks arising from AI are inherently international in nature, and so are best addressed through international cooperation. We resolve to work together in an inclusive manner to ensure human-centric, trustworthy and responsible AI that is safe, and supports the good of all through existing international fora and other relevant initiatives, to promote cooperation to address the broad range of risks posed by AI. In doing so, we recognise that countries should consider the importance of a pro-innovation and proportionate governance and regulatory approach that maximises the benefits and takes into account the risks associated with AI. This could include making, where appropriate, classifications and categorisations of risk based on national circumstances and applicable legal frameworks. We also note the relevance of cooperation, where appropriate, on approaches such as common principles and codes of conduct. With regard to the specific risks most likely found in relation to frontier AI, we resolve to intensify and sustain our cooperation, and broaden it with further countries, to identify, understand and as appropriate act, through existing international fora and other relevant initiatives, including future international AI Safety Summits.

All actors have a role to play in ensuring the safety of AI: nations, international fora and other initiatives, companies, civil society and academia will need to work together. Noting the importance of inclusive AI and bridging the digital divide, we reaffirm that international collaboration should endeavour to engage and involve a broad range of partners as appropriate, and welcome development-orientated approaches and policies that could help developing countries strengthen AI capacity building and leverage the enabling role of AI to support sustainable growth and address the development gap.

We affirm that, whilst safety must be considered across the AI lifecycle, actors developing frontier AI capabilities, in particular those AI systems which are unusually powerful and potentially harmful, have a particularly strong responsibility for ensuring the safety of these AI systems, including through systems for safety testing, through evaluations, and by other appropriate measures. We encourage all relevant actors to provide context-appropriate transparency and accountability on their plans to measure, monitor and mitigate potentially harmful capabilities and the associated effects that may emerge, in particular to prevent misuse and issues of control, and the amplification of other risks.

In the context of our cooperation, and to inform action at the national and international levels, our agenda for addressing frontier AI risk will focus on:

identifying AI safety risks of shared concern, building a shared scientific and evidence-based understanding of these risks, and sustaining that understanding as capabilities continue to increase, in the context of a wider global approach to understanding the impact of AI in our societies.

building respective risk-based policies across our countries to ensure safety in light of such risks, collaborating as appropriate while recognising our approaches may differ based on national circumstances and applicable legal frameworks. This includes, alongside increased transparency by private actors developing frontier AI capabilities, appropriate evaluation metrics, tools for safety testing, and developing relevant public sector capability and scientific research.

In furtherance of this agenda, we resolve to support an internationally inclusive network of scientific research on frontier AI safety that encompasses and complements existing and new multilateral, plurilateral and bilateral collaboration, including through existing international fora and other relevant initiatives, to facilitate the provision of the best science available for policy making and the public good.

In recognition of the transformative positive potential of AI, and as part of ensuring wider international cooperation on AI, we resolve to sustain an inclusive global dialogue that engages existing international fora and other relevant initiatives and contributes in an open manner to broader international discussions, and to continue research on frontier AI safety to ensure that the benefits of the technology can be harnessed responsibly for good and for all. We look forward to meeting again in 2024.

The countries represented were:

References to 'governments' and 'countries' include international organisations acting in accordance with their legislative or executive competences.

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The Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023

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Artificial Intelligence ( AI) presents enormous global opportunities: it has the potential to transform and enhance human wellbeing, peace and prosperity. To realise this, we affirm that, for the good of all, AI should be designed, developed, deployed, and used, in a manner that is safe, in such a way as to be human-centric, trustworthy and responsible.

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Replacement for Bard in February 2023. Googles new ' large multi-modal model' (LMM) works directly with multiple input / output modes. As well as supporting text which Bard and GPT do, it supports images, audio and video without translating the information to text before processing it. A new acronym is emerging: LMM to replace large language model LLM.

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‎Gemini - chat to supercharge your ideas

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Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI.

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PACT is an alternative to PREP:

PACT

* P: Persona

* A: Action (Task)

* C: Context

* T: Target (Format)

PACT is your roadmap to crafting effective prompts.

Clearly define the Persona you want the AI to adopt (e.g., a seasoned scientist, social media manager, a witty comedian).

Then, specify the exact Action you need it to perform (summarize, write, translate).

Next, provide essential Context to guide the AI's understanding (topic, style, audience).

Finally, outline the desired Target format for the output (blog post, script, poem).

By following the PACT prompt framework, you'll significantly improve your chances of getting the AI output you desire.

Here’s an example.

Scenario: A fashion brand wants to launch a new sustainable clothing line and needs engaging social media content.

PACT Prompt:

Persona: You are a passionate environmentalist and fashion influencer.

Action / Task: Create a captivating Instagram caption.

Context: Highlight the eco-friendly materials used in our new clothing line, emphasizing sustainability and style. Target a young, environmentally conscious audience.

Target/ format”: A short, snappy caption with a strong call to action to shop the collection.

As usual finish the prompt with Please ask me any clarification questions?

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Anthropic's next-generation AI assistant based on research into training helpful, honest, and harmless AI systems.

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Talk with Claude, an AI assistant from Anthropic

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As with all AI language models, always verify the response given using known, reputable sources.

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