• Read the first section in part 2 below, Key Terminology & Principles, which gives the essential introduction to some of the key concepts

• Go and visit one of the LLM apps identified in the second section below (e.g. ChatGPT, Google Bard, Bing). You may need to register for these. 

• Once registered, or in the app try some of the following prompts:

  • "Hello. I am a public health professional in the UK with an interest in [insert subject area]. Can you please tell me how AI could help me in my job and then more specifically, how it could help me undertake research in this area?"

  • "That's great. Could you now give me more specific insight into how a Large Language Model AI tool like Bard or ChatGPT could help me?"

  • "I'm planning on writing a literature review for a research project. The intended report is on [insert example: e.g. the effectiveness of a whole school physical activity approach in raising activity levels in children and their families]. Can you please outline a plan for this literature review?"

• Read the section in part 2 below, Getting the most out of AI: Prompts, which includes a range of ideas for using generative AI and ways to ask for information

• Go to your favoured LLM tool (e.g. ChatGPT, Google Bard, Bing) and try out some new prompts or tasks.

  • Maybe try a question like this: "Please outline how I could use Appreciate Inquiry to engage with local stakeholders on a project related to a compassionate approach to weight management service provision in public health" - or change any of the content above to suit your area of interest. 

The tasks below are designed to help you reflect on your use of generative AI. Once you have reflected, it would be great to share that insight. Read the list and then choose any of the tasks.  

• Read the section in part 2 below, Getting the most out of AI: Prompts, which includes a range of ideas for using generative AI and ways to ask for information. Try something new. 

• Review the papers posted in the Further Reading section below. Make notes on your response.

• Make a list of your favourite 3 uses of AI in research and share it with someone else.

• Identify your favourite AI-based apps for research, put them in order of usefulness, add a note next to each as to why it is so useful. Share that with others.

• 10 min research proposal task: Using AI tools, write the best research proposal that you can in ten minutes.

• Literature search: Search online for examples of how AI has been used in the research process in your field of interest.

This whole page is an introduction to the use of AI but in this section are included some of the key terms and a few introductory resources. 

A Brief Glossary

• Artificial Intelligence (AI) involves using computers to do things that traditionally require human intelligence. AI can process large amounts of data in ways that humans cannot. The goal for AI is to be able to do things like recognize patterns, make decisions, and judge like humans. AI is not new and AI is a much broader field than LLMs and ChatGPT. It can be used in many ways AND we have all been using it for years (e.g. music streaming, search engines, social media, online shopping, online advertising)

• Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, or music. It does this by learning from a large dataset of existing content and then using that knowledge to generate new, similar content. This could be text, images, music or 3D objects

• Large Language Models (LLMs) are a type of AI that generate and understand human languages. They are an advanced chatbot that can also trawl the internet and quickly produce succinct answers to complex questions. They are trained on massive datasets of text and code, and they can be used for a variety of tasks, including text generation, summarisation, question answering, translation and natural language inference where it can be used to determine the meaning of text.

• ChatGPT (Google Bard, Bing Chat, Pi etc) are all apps/tools that allow users to engage with a LLM and ask it questions. These are the user interfaces.

Key Principles for using AI for research

• AI (ChatGPT/Bing/Bard) is great at presenting solutions. Also, it has many limitations. Make sure you are aware of those.

• If you provide poor prompts, you will get poor results from it. The questions you ask of it will need refining and a degree of prior knowledge.

• Learning to use AI is an emerging skill. Being better at using it will stand you in good stead in the future.

• Don't trust anything it says. You will be responsible for using anything that it presents to you and you should check all content and facts and have a level of knowledge on a topic to know what is useable and what is not. 

• Be thoughtful about when this tool is useful. Don't use it if it isn't appropriate. 

What is AI good for?

• Answering questions where answers are based on material which can be found on the internet. 

• Drafting ideas and planning or structuring written materials. 

• Generating ideas for graphics, images and visuals. 

• Reviewing and critically analysing written materials to assess their validity. 

• Helping to improve your grammar and writing structure – especially helpful if English is a second language. 

• Experimenting with different writing styles. 

• Getting explanations. 

• Getting over writer’s block - rather than stare at a blank page, ask it a question and get something down on paper like an essay outline. 

• Improve your critical analysis and evaluation skills - by studying and critiquing what they produce, and making judgements about whether what they produce is actually valid and believable. 

The Limitations of using AI/LLMs

Artificial intelligence and human intelligence are not the same; AI tools do not understand anything they produce nor do they understand what the words they produce mean when applied to the real world.  

• AI tools frequently get things wrong and can’t be relied upon for factual accuracy. 

• AI produces a very generic, often surface-level response which can appear automated in style.

• They perform better in subjects which are widely written about, and less well in niche or specialist areas. 

• Unlike a normal internet search, they don’t look up current resources and are currently some months out of date. 

• They struggle to provide reliable references – they tend to produce well-formatted but fictitious citations. 

• They have been said to perpetuate stereotypes, biases and Western perspectives.

• Over-reliance on these tools could reduce your opportunities to hone your writing, critical thinking, and evaluation skills.

Further Introductory Resources

• Lingard, L. (2023). Writing with ChatGPT: An Illustration of its Capacity, Limitations & Implications for Academic Writers. Perspectives on Medical Education, 12(1), 261. https://doi.org/10.5334%2Fpme.1072 

This section includes a list of tools and apps that allow relatively easy use of AI. 

AI/Large Language Models

• ChatGPT - https://openai.com/blog/chatgpt - v 3 is FREE (inconsistency access, not as clever), v4.0 is ~$20/month (and is always accessible and more powerful)

• Google Gemini- https://gemini.google.com/app - use your google log-in, you might need to register to be part of it. Very good access, similar to ChatGPT

• Notebook LM - https://notebooklm.google/ - another Google tool. Great for uploading complex documents and interrogating or cross-referencing them. Also great for making study notes or a podcast of people talking about the uploaded documents.

• Bing - You must be using Microsoft Edge as your browser. Look for the "chat" tab on the Bing homepage.  If you have trouble accessing Chat...make sure you are signed in with your Microsoft log-in. If you can't use it due to SafeSearch, go into settings and switch away from Safesearch. Other tips include...make sure that you have the latest version of the Bing app installed, clear your browser’s cache and cookies, and restart your device, if the issue persists, try uninstalling and reinstalling the app, consider setting Microsoft Edge as your default browser.   

• Perplexity - https://www.perplexity.ai/ - Free to use, although a paid option allows more access to the CoPilot feature. This tool appears to be accessing more up-to-date searches of the internet than GPT-3 and makes use of GPT-4 within the free access, albeit in a limited manner. It also appears to offer direct links to its sources in its output. 

  • A comparison of ChatGPT, Bard and Bing - https://www.theverge.com/2023/3/24/23653377/ai-chatbots-comparison-bard-bing-chatgpt-gpt-4

  • Mollick, E. (2023, 7 Dec) An Opinionated Guide to Which AI to Use: ChatGPT Anniversary Edition.” One Useful Thing substack. https://www.oneusefulthing.org/p/an-opinionated-guide-to-which-ai 

Apps that use AI

• Consensus - "Ask a question, get conclusions from research papers" - https://consensus.app/search/ (Seems good for signposting to good sources AND providing snippets from each. )

• Elicit - Another tool that can be asked questions and will provide references and signposting to papers https://elicit.org/  [A review of Elicit - May 2023]

• SciSpace - https://typeset.io/ - ask a question and get summaries from key papers. Add columns of information as required. [A review of SciSpace - Aug 2023]

• Wonders - https://app.readwonders.com/ - another app for finding information

• Evidence Hunt - https://evidencehunt.com/ - Ask a question and it will be turned into a literature search of PubMed. Outputs are listed as abstracts that are presented with a PICO analysis overlayed so each key element of the abstract is highlighted a different colour. 

• The Literature -  https://www.the-literature.com - similar to Evidence Hunt in that it searches PubMed but results can be presented in a range of ways like SciSpace

• Research Rabbit - find new papers and make connections - https://www.researchrabbit.ai/

• Connected papers - explore connected papers in visual graphs - https://www.connectedpapers.com/ 

• Carrot2 - Similar to above - https://search.carrot2.org/#/search/web 

• Scholarcy - An AI powered article summariser, builds interactive flashcards of articles - https://www.scholarcy.com/ 

• Paper Digest - An AI powered article summariser - https://www.paper-digest.com/ 

• Laterl.io - AI article summariser - https://www.lateral.io/ 

• Inciteful.xyz - Find papers, make connections, export to Zotero - https://inciteful.xyz/ 

• Quillbot  - A free paraphrasing website - https://quillbot.com/

• Ryte.me - an AI writing tool - https://rytr.me/ 

• Paperpal - an AI writing tool - https://paperpal.com/ 

• writefull - an AI writing tool - https://www.writefull.com/ 

• Vizcom - produce a line drawing, refine the image description with words and allow the app to render a full version of your desired image  - https://app.vizcom.ai 

• Pi.ai - An AI chatbot that you can talk to. On iOS it can hear your voice and maintain a conversation, On Windows and Android, it responds to typed chat but can respond with voice. Maybe use it to get feedback on your initial ideas https://pi.ai/talk 

Generative AI can be used in many appropriate ways but it could also be used inappropriately and in a way that is contrary to recognised values and principles of research practice.

The following section outlines some key points in this area.

Further Reading

Kaebnick, G. E., Magnus, D. C., Kao, A., Hosseini, M., Resnik, D., Dubljević, V., ... & Diniz, D. (2023). Editors’ statement on the responsible use of generative AI technologies in scholarly journal publishing. Ethics & Human Research, 45(5), 39-43. https://doi.org/10.1002/hast.1507 

Elsevier (n.d.) The use of generative AI and AI-assisted technologies in writing for Elsevier. https://www.elsevier.com/en-gb/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier 

Signs that text might have been written using AI

Adapted from: https://medium.com/@marginaliant/5-signs-a-students-essay-has-been-written-using-ai-a569235ab1be

• Lack of Clarity and Coherence - One of the most common signs that an essay has been written using AI is a lack of clarity and coherence. AI-generated essays often lack a clear thesis statement and tend to be disjointed, with no clear connection between the different paragraphs.

• Repetitive Content - Another sign of an AI-generated essay is repetitive content. Because AI algorithms rely on pre-existing data to generate essays, they often end up repeating the same points over and over again, without adding any new insights or ideas.

• Grammatical Errors - Despite the advances made in AI technology, grammar and syntax remain a challenge for most AI algorithms. As such, essays generated using AI are often rife with grammatical errors and awkward sentence structures.

• Lack of Originality - AI-generated essays are notorious for their lack of originality. Since they rely on pre-existing data, they tend to regurgitate the same ideas and arguments that have been made in the past, without adding anything new or innovative to the conversation.

• Unnatural Language - Finally, essays generated using AI often feature unnatural language that sounds stilted and robotic. This is because AI algorithms are still not advanced enough to replicate the nuances and complexities of human language, resulting in essays that lack a human touch.

• Incorrect Referencing - Early versions of ChatGPT/LLMs have struggled to provide accurate citations. They can look correct, but may well be reconstituted and adapted citations that use real authors and journal titles but meld them together into manufactured sources. This inability will no doubt be addressed over time however, examination of citations and references could demonstrate poor scholarship and potentially use of AI. 

Tasks/Prompts for producing written reports and doing research

• Get help starting your work: Ask it to provide an outline for your work, e.g. a plan for...

• Get help with referencing: Paste in a bunch of references and ask it to ensure they are all in line with APA7 (for example) 

• Get help with critical analysis: Ask it to outline a critical analysis of a particular theory, approach or technique (the response will need thinking about, it won't come with specific underpinning references, it might be in the wrong tone, but it might give you some thoughts on key potential critiques).

• Get help with the quality of your written prose: Once you have written some text that includes the key points that you want to communicate, and your references you could try asking it to "improve the quality of the writing in this piece of academic writing". The output will need thinking about but there may be some useful changes. it might even tell you what changes it has made which could help you improve your own ability to write in the long term. 

• Get help consolidating topics and revising/reviewing: Ask it to produce 10 multi choice revision questions on X, or "review questions on X".

• Get help with your writing: Ask it to, "Review and if required, improve, the reporting verbs in this paragraph - insert para".

• Get help producing an action plan in response to previous feedback: Ask it "What can I do in response to the feedback on this work - insert feedback".

• Get help with research methods: e.g. "Please outline how I could use Appreciate Inquiry to engage with local stakeholders on a project related to a compassionate approach to weight management service provision in public health"

• Raise your game preparing for that dream job: Ask it to write a cover letter for a job and include a link to your CV, a link to the job advert and a link to the JD [Google Bard accepts links to google docs]".

• Get help with your stats: give it some data or a link to a spreadsheet and ask it to perform some analysis, or ask it what analysis might be suitable. 

• Get help with your qualitative research: Ask AI to produce an interview guide for a semi-structured interview in a study that uses qualitative research methods. (this first will not be good enough to use but might be a good start that is better than staring at a blank page).

• Produce outline meeting agenda and minutes: AI/LLMs can be used to review notes and content such as previous minutes and then produce an outline meeting agenda. They can also produce minutes by inputting notes and links to documents, also potentially using transcripts of meetings. 

• Use AI to produce many varied examples: People often need many examples when learning complicated concepts but these can take time to produce. 

  • Copy this text prompt into Google Bard. The reply should ask you some questions about your needs and then you can be more specific about the topic and audience for your examples. "I would like you to act as an example generator for learners/students. When confronted with new and complex concepts, adding many and varied examples helps students better understand those concepts. I would like you to ask what concept I would like examples of, and what level of students I am teaching. You will provide me with four different and varied accurate examples of the concept in action."

More on prompts and the use of AI in research

• Compton, M. (live doc) Sandpit: Testing the capabilities of chaptGPT - examples of reading, precising, reformatting text and refs, tabulation, rubric, feedback and marking https://docs.google.com/document/d/1K_UgkLt6--Bqv_FViREBvRzXbD4yR4LzHep15caln4U/edit#heading=h.4uc5kd2g4low

• Langston, T. (2023, June) Last Night ChatGPT Saved My Life… [A blog about a PG student's use of AI] https://teltales.port.ac.uk/2023/06/26/guest-blogger-tom-langston-last-night-chatgpt-saved-my-life

• Shah, S. (2023, October) Prompt Tuning: A Powerful Technique for Adapting LLMs to New Tasks. Medium. https://medium.com/@shahshreyansh20/prompt-tuning-a-powerful-technique-for-adapting-llms-to-new-tasks-6d6fd9b83557 

Various AI-powered applications can be useful for different aspects of the research process. Before selecting an AI app for your research, consider the specific requirements of your study, the type of data you need to collect, and the analysis methods you plan to use. Additionally, ensure that the chosen app complies with ethical standards and data protection regulations relevant to your research.

Research Design

• de Silva, D. & el Ayoubi, M. (2023, June 20). Three ways to leverage ChatGPT and other generative AI in research. Times Higher Education. https://www.timeshighereducation.com/campus/three-ways-leverage-chatgpt-and-other-generative-ai-research   - ideas for how to use ChatGPT to sharpen your research question and design.

Literature Reviews

Many AI-powered apps can assist with the literature review. Check out the 25 prompts for ChatGPT below as well, for ideas of how to frame your prompts. We have used the following two platforms and have found them sueful:

• Semantic Scholar - an impressive free, AI-powered research tool for scientific literature, able to search over 215 million academic papers. https://www.semanticscholar.org/ You can adjust the date range, ask for articles only with open pdf access, limit to certain subject areas, and sort according to number of citations, how influential a paper has been, or most relevant to your keyword search. Very useful for an initial sweep of the literature to inform a research project. 

• NotebookLM - free - A Google tool that can offer critical analysis of a paper you upload, then compare multiple papers, then make a podcast discussing whatever you want, like a discussion about a few papers. https://notebooklm.google/ 

• Research Rabbit -  Free - find new papers and make connections - https://www.researchrabbit.ai/

• Carrot2 - Free - Try the Treemap feature when you have searched for documents - https://search.carrot2.org/#/search/web 

• Litmaps - Free - allows researchers to visualise relationships between papers and find derivative works - https://www.litmaps.com/ 

• Inciteful.xyz - Find papers, make connections, export to Zotero - https://inciteful.xyz/ 

• Elicit - Another tool that can be asked questions and will provide references and signposting to papers https://elicit.org/  [A review of Elicit - May 2023]

• Consensus - "Ask a question, get conclusions from research papers" - https://consensus.app/search/ (Seems good for signposting to good sources AND providing snippets from each. )

• TLDRthis - can provide summaries of large amounts of text - https://www.tldrthis.com/ 

• Askyourpdf - can provide summaries of large amounts of text - https://askyourpdf.com/ 

• Powerdrill - can help to highlight overlooked or underexplored study areas

• SciSpace - can help generate a preliminary literature review - https://typeset.io/ - ask a question and get summaries from key papers. Add columns of information as required. [A review of SciSpace - Aug 2023]

• Jenni.ai - can help generate a preliminary literature review

ChatGPT

Seb Dianati and Suman Laudari suggest 25 prompts to ask ChatGPT to help with your research. Read their article on Times Higher Education (you can sign up for free to access articles):

• Dianati, S. & Laudari, S. (2023, October 24). ChatGPT and generative AI: 25 applications to support research. Times Higher Education. https://www.timeshighereducation.com/campus/chatgpt-and-generative-ai-25-applications-support-research 

Data Extraction

• MonkeyLearn - https://monkeylearn.com/ - text analysis and machine learning from data produced in tweets, documents, and surveys.

Transcription

If you have a lot of interviews or focus groups to be transcribed for your research, it will likely be necessary to pay for transcription services if you do not wish to do it yourself. Some of the AI-powered apps offer free trials or limited free use per month.

• Otter.ai - automatically transcribes interviews, meetings, and other audio sources for qualitative data analysis. Free tier available.

• Microsoft Teams can transcribe online meetings.

• Rev.com - is a paid transcription service that combines AI and human transcribers for quality control. 

Data Analysis

• Perplexity.ai - ask it for advice on your intended analysis e.g. https://www.perplexity.ai/search/can-you-explain-wwQUoRdDRfS2r6fYWCy7Bw 

• R (The R Project for Statistical Computing)Links to an external site. - R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

• IBM SPSS Statistics: statistical analysis software that analyses and interprets collected data for research purposes.

• NVivo: qualitative data analysis software that can collect, organise, and analyse qualitative data such as interviews, focus groups, and surveys.

• Microsoft Excel (with Power Query and Power Pivot): spreadsheet software that can create custom data collection forms, and use advanced features for data analysis.

• Tableau: data visualisation software (one year free for students) - use it to visualise and analyse research data, and create interactive dashboards. Some useful free online learning about Data LiteracyLinks to an external site. (you will need to set up a free account to access)

• Julius: https://julius.ai/ 

Writing

• ChatGPT - https://openai.com/blog/chatgpt 

• Perplexity - https://www.perplexity.ai/ 

• Google Bard - https://bard.google.com/

• SciSpace - https://typeset.io/ 

Others

• Hey Science (an AI reviewer) - https://reviewer.heyscience.ai/ 

Overview

Generative AI has the potential to be a significant step change in the way we develop statistical competence and understanding offering support to researchers and analysts across various skill levels. By leveraging AI models' natural language processing capabilities, users can use genAI as a study buddy or expert peer asking questions on anything from data preparation to interpretation of results. This integration of AI into statistical practice has the potential to improve efficiency and understanding and make certain methods of analysis more accessible to a broader audience.

Here are some useful approaches for incorporating generative AI into statistical analysis, along with sample prompts:

Data Preparation and Cleaning

• Identifying outliers: "Describe methods for detecting outliers in a dataset of [specific type] and provide example code in R or Python*."

  • • * In any of these examples, consider asking how to do something in the software that you are using

• Handling missing data: "Explain different approaches for dealing with missing values in a dataset, including their pros and cons."

Exploratory Data Analysis

• Generating descriptive statistics: "Summarize the key descriptive statistics I should calculate for a dataset containing [variables], and explain how to interpret each."

• Visualizing data: "Suggest appropriate data visualizations for exploring relationships between [variables] and provide sample code in ggplot2 or matplotlib."

Statistical Test Selection

• Choosing appropriate tests: "I am planning a research project that will [explain as much as possible about the project] and data will include [variables]. What statistical test(s) would be most appropriate, where in the study and why?"

• Understanding assumptions: "Explain the assumptions underlying [specific statistical test] and how to check if they are met."

Interpreting Results

• Interpreting results: "How do I interpret the outputs of a one-way ANOVA completed in SPSS?"

• Explaining p-values: "Provide a simple explanation of what a p-value of [value] means in the context of a [specific test]."

• Interpreting effect sizes: "Describe how to calculate and interpret Cohen's d for a study comparing [groups] on [variable]."

Advanced Techniques

• Machine learning basics: "Explain the difference between supervised and unsupervised learning, providing examples of each in a [specific field] context."

• Model selection: "Compare and contrast logistic regression and decision trees for predicting [outcome] based on [predictors]."

Reporting and Visualisation

• Using the right language: "Please give an example of how I could report the findings of [insert type of analysis]"

• Creating effective tables: "Provide guidelines for creating a clear and informative table to present results of a [specific analysis]."

• Designing impactful figures: "Suggest ways to visually represent the results of a multiple regression analysis predicting [outcome] from [predictors]."

Troubleshooting and Error Interpretation

• Debugging code: "Explain common reasons for receiving a [specific error message] in R/Python and how to resolve it."

• Interpreting unexpected results: "What might cause a regression analysis to show no significant relationship between [variables] when theory suggests there should be one?"

Suggested Tools

• Perplexity.ai - ask it for advice on your intended analysis e.g. https://www.perplexity.ai/search/can-you-explain-wwQUoRdDRfS2r6fYWCy7Bw 

• R (The R Project for Statistical Computing) - https://www.r-project.org/ - R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

• IBM SPSS Statistics: statistical analysis software that analyses and interprets collected data for research purposes.

• NVivo: qualitative data analysis software that can collect, organise, and analyse qualitative data such as interviews, focus groups, and surveys.

• Microsoft Excel (with Power Query and Power Pivot): spreadsheet software that can create custom data collection forms, and use advanced features for data analysis.

• Tableau: data visualisation software - https://www.tableau.com/en-gb/academic/students - use it to visualise and analyse research data, and create interactive dashboards. 

• Julius: https://julius.ai/ 

Find out more

• Can You Use ChatGPT to Read CSV Files? https://expertdatatips.com/can-you-use-chatgpt-to-read-csv-files-593ccfbf1cca 

• Realizing the potential of generative AI in human services: Use cases to transform program delivery https://www2.deloitte.com/us/en/insights/industry/public-sector/automation-and-generative-ai-in-government/generative-ai-in-public-health.html 

• Biswas, S. S. (2023). Role of chatgpt in public health. Annals of biomedical engineering, 51(5), 868-869. https://doi.org/10.1007/s10439-023-03172-7 

• Fisher, S., & Rosella, L. C. (2022). Priorities for successful use of artificial intelligence by public health organizations: a literature review. BMC Public Health, 22(1), 2146. https://doi.org/10.1186/s12889-022-14422-z 

• Flaxman, A. D., & Vos, T. (2018). Machine learning in population health: opportunities and threats. PLoS medicine, 15(11), e1002702. https://doi.org/10.1371/journal.pmed.1002702 

• Giansanti, D. (2022). Artificial intelligence in public health: current trends and future possibilities. International Journal of Environmental Research and Public Health, 19(19), 11907. https://doi.org/10.3390/ijerph191911907 

• Jungwirth, D., & Haluza, D. (2023). Artificial intelligence and public health: an exploratory study. International Journal of Environmental Research and Public Health, 20(5), 4541. https://doi.org/10.3390/ijerph20054541 

• Morita, P. P., Abhari, S., Kaur, J., Lotto, M., Miranda, P. A. D. S. E. S., & Oetomo, A. (2023). Applying ChatGPT in public health: a SWOT and PESTLE analysis. Frontiers in Public Health, 11, 1225861.https://doi.org/10.3389/fpubh.2023.1225861 

• Morita, P., Abhari, S., & Kaur, J. (2023). Do ChatGPT and Other Artificial Intelligence Bots Have Applications in Health Policy-Making? Opportunities and Threats. International Journal of Health Policy and Management. https://doi.org/10.34172/ijhpm.2023.8131 

• O’Mara-Eves, A., Thomas, J., McNaught, J., Miwa, M., & Ananiadou, S. (2015). Using text mining for study identification in systematic reviews: a systematic review of current approaches. Systematic reviews, 4(1), 1-22. https://doi.org/10.1186/2046-4053-4-5 

• Pan American Health Organisation (2021) Artificial Intelligence in Public Health: Digital Transformation Toolkit. https://iris.paho.org/handle/10665.2/53732 

• Smith, M. J., Axler, R., Bean, S., Rudzicz, F., & Shaw, J. (2020). Four equity considerations for the use of artificial intelligence in public health. Bulletin of the World Health Organization, 98(4), 290. https://doi.org/10.2471%2FBLT.19.237503 

• Sood, T., Sharma, E., & Katoch, G. (2023). Scope and Challenges of AI in Public Health. Journal of the Epidemiology Foundation of India, 1(1). https://efi.org.in/journal/index.php/EFIjournal/article/view/121/95 

• Weiss, D., Rydland, H. T., Øversveen, E., Jensen, M. R., Solhaug, S., & Krokstad, S. (2018). Innovative technologies and social inequalities in health: a scoping review of the literature. PloS one, 13(4), e0195447. https://doi.org/10.1371/journal.pone.0195447 

• Brown, R. / DEMOS (2023, Nov) The AI Generation: How universities can prepare students for the changing world https://demos.co.uk/research/the-ai-generation-how-universities-can-prepare-students-for-the-changing-world/ 

• Bryda, G. & Costa, A.P. (2023). Qualitative Research in Digital Era: Innovations, Methodologies and Collaborations. Soc. Sci., 12(10), 570. https://doi.org/10.3390/socsci12100570 

• Perkins, M. (2023). Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2). https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3071&context=jutlp

• Eaton, S.E. (2023) 6 Tenets of Postplagiarism: Writing in the Age of Artificial Intelligence https://drsaraheaton.wordpress.com/2023/02/25/6-tenets-of-postplagiarism-writing-in-the-age-of-artificial-intelligence/

• Grove, J. (2023, Sept 22). Will ChatGPT Transform Research? It Already Has, Say Nobelists. Inside Higher Ed. https://www.insidehighered.com/news/global/2023/09/22/nobel-prize-winners-say-chatgpt-has-already-transformed-research

• Milles, A. (live document) How Well Can AI Respond to My Assignment Prompts? (A working document with prompt ideas AND reflections on how assignment briefs can be adjusted in light of ChatGPT) https://docs.google.com/document/d/1ZbrdqB2xqoOVOdo2OAbk9Osz4_xyG7Xhp2RpeJyWG0g/edit#heading=h.il4v6contqkx

• Mills, S., Costa, S., & Sunstein, C. R. (2023). The Opportunities and Costs of AI in Behavioural Science. Available at SSRN 4490597. http://www.law.harvard.edu/programs/olin_center/papers/pdf/Sunstein_1104.pdf 

• van Noorden, R. & Perkel, J.M.  (2023, Sept 27). AI and science: what 1,600 researchers think. Nature https://www.nature.com/articles/d41586-023-02980-0 

• UKRIO (2023, July) "I can’t help falling in love with AI”: chatbots and research integrity. https://ukrio.org/ukrio-resources/ai-in-research/ 

• University of Sheffield Centre for Machine Intelligence (n.d.) AI-Enabled Research https://www.sheffield.ac.uk/machine-intelligence/research-themes/ai-enabled-research 

Recommended Resources

• Machines and Society - An NYU Shanghai Resource - https://guides.nyu.edu/data/home