Useful graphic from @nonmayorpete (via Zvi Mowshowitz). What we need now I suspect is some academic reflections on use of tools like this:
This is the best list of examples I could get out of ChatGPT-4. The web browsing functionality means it can now identify real tools and services which would not have been in the original training data:
- Course Material Generation: Generative AI can be used to create course materials such as quizzes, exams, and homework assignments. This can be a significant time-saver for teaching staff. For example, a tool like Quillionz can use AI to generate questions based on the course material. The teacher provides a text (e.g., a chapter of a textbook), and the AI generates multiple-choice, fill-in-the-blank, and other types of questions. This can reduce the time spent on creating quizzes and allow educators to focus on more high-value tasks such as lesson planning and student engagement.
- Automated Feedback: AI tools like Turnitin’s Gradescope utilize machine learning to give automated, consistent feedback on student assignments, especially useful for larger classes. The AI is trained to understand a rubric provided by the educator and applies it to grade student assignments. This not only saves time but also ensures objectivity and consistency in grading.
- Research Paper Drafting: Tools like EndNote or Scholarcy can assist in literature review and reference management, but newer AI tools go even further. For example, an AI tool like SciNote can generate a draft of a scientific paper based on the data and notes provided, which could save researchers significant time and effort in writing and formatting their initial drafts.
- Data Analysis: In research, AI tools can be used to analyze data and identify patterns. Tools like RapidMiner or DataRobot can take large datasets and use machine learning algorithms to predict outcomes, classify data, or find correlations. This can save researchers a significant amount of time compared to traditional data analysis methods.
- Simulations and Modeling: Generative AI can be used to create complex simulations and models, which can be particularly useful in fields like physics, biology, and economics. For instance, GPT-3 could be used to generate simulations of economic models based on input parameters, making it easier for researchers to test various hypotheses and scenarios.
- Literature Search and Review: AI tools like Iris.AI can expedite the process of literature search and review by reading and understanding multiple articles and summarizing the main points. This can be a significant time-saver for researchers who need to stay up-to-date with the latest research in their field.
- Plagiarism Detection: AI tools like Turnitin can compare a student’s work against a vast database of academic papers, books, and websites to detect possible plagiarism. This allows teaching staff to ensure academic integrity without having to manually compare each student’s work to potential sources.
- Chatbots for Student Support: AI chatbots can provide 24/7 support to students, answering frequently asked questions about course material, assignments, deadlines, etc. This can reduce the workload on teaching staff and provide quicker responses to students.
And some more:
- Automated Lecture Transcription and Subtitling: Generative AI tools like Otter.ai or Google’s Live Transcribe can transcribe lectures in real time, making the content more accessible for students who are deaf or hard of hearing, or for whom English is a second language. This transcription can also be used to generate notes, study guides, or review materials.
- Predictive Analysis in Student Success: AI tools like Civitas Learning use predictive analytics to identify students who may be at risk of failing or dropping out. These tools analyze data like grades, attendance, and engagement to predict student outcomes and enable early interventions.
- Enhanced Learning through Virtual Reality: AI can be used in combination with virtual reality (VR) to create immersive learning experiences. For example, Labster provides virtual laboratory experiences enhanced by AI. The AI can generate different scenarios based on student input, provide guidance, and assess student performance.
- Automated Language Translation: AI tools like DeepL or Google Translate can automatically translate academic papers, lectures, or other educational materials, making them accessible to international students or researchers.
- Assistance in Coding and Programming: AI-powered tools like Kite or Codota can help students learn programming by providing real-time assistance as they write code. These tools can suggest code snippets, identify errors, and offer solutions, enhancing the learning experience and making coding more accessible for beginners.
- Synthetic Data Generation: In some cases, researchers may need data that doesn’t exist or is hard to obtain. Generative AI models, like Generative Adversarial Networks (GANs), can create synthetic data that can be used for preliminary analysis, hypothesis testing, or model training.
- Artificial Intelligence in Creative Writing: Generative AI models like OpenAI’s GPT-3 can assist in creative writing tasks. For instance, they could be used in a creative writing class to provide students with unique writing prompts, continue a story in a particular style, or generate examples of certain literary techniques.
- Interactive AI Tutors: Intelligent tutoring systems like Carnegie Learning’s MATHia use AI to provide personalized instruction to students. These systems can adapt to a student’s learning style and pace, provide hints and explanations, and give feedback on performance.
- Automated Citation and Bibliography Generation: Tools like Zotero, EndNote, or CiteThisForMe can automate the process of citation and bibliography creation, a task that can be time-consuming and prone to errors. These tools use AI to accurately format citations in a variety of styles based on a URL, DOI, ISBN, or other identifiers.
And some more:
- Automated Statistical Analysis: Tools such as JASP and SPSS use AI algorithms to perform complex statistical analyses on research data. These tools can generate visualizations, conduct hypothesis testing, perform regression analyses, and more. They can also suggest the most appropriate statistical tests based on the nature of the data, making them valuable tools for researchers.
- Document Summarization: AI tools can automatically summarize long documents, such as research papers or textbooks. This can be useful for students who need to quickly understand the key points of a document, or for researchers who need to review a large number of papers. Tools like Scholarcy and SMMRY use AI to extract the most important information from a document and present it in a condensed format.
- Semantic Search: Tools like Semantic Scholar use AI to improve the process of searching for academic papers. They can understand the content of papers and provide more relevant search results based on the searcher’s intent, making it easier for researchers to find the information they need.
- Text-to-Speech Software: AI-powered text-to-speech tools can read out written content, making academic materials more accessible for students with visual impairments or learning disabilities. Tools like Google’s Text-to-Speech or Microsoft’s Azure Cognitive Services can convert text into natural-sounding speech in a variety of languages and voices.
- Facial Recognition for Attendance: Some universities are using AI-powered facial recognition technology to automate attendance taking in large lectures. This can save time for professors and ensure a more accurate record of student attendance.
- AI in Music and Art Education: In creative fields, AI can be used to generate music or art, providing students with new ways to learn and create. Tools like AIVA or DeepArt use AI to compose music or create artwork based on a given input, which can be used to teach students about composition, harmony, color theory, and more.
- Automated Proofreading: Tools like Grammarly use AI to detect grammar, punctuation, and spelling errors in a text. They can also suggest improvements in word choice, tone, and style. This can be a valuable tool for students and researchers who are writing papers, theses, or dissertations.
- AI in History and Archaeology: AI can be used to analyze historical texts, artifacts, or archaeological sites. For example, tools like Google’s Cloud Vision API can identify objects in images, which could be used to classify artifacts or analyze historical photos. Similarly, AI can be used to translate ancient languages or decipher handwritten texts, aiding researchers in their studies.
I then asked for a summary of these possibilities using the categories teaching, research, administration and public engagement. Interestingly, it struggled with the latter (though watching it modulate the search term in increasingly broad ways was fascinating):
- Course Material Generation: AI can generate quizzes, exams, and homework assignments, saving time for educators【23†source】.
- Automated Feedback: AI can provide consistent feedback on student assignments, ensuring objectivity and consistency in grading【23†source】.
- Chatbots for Student Support: AI chatbots can provide 24/7 support to students, reducing the workload on teaching staff and providing quicker responses to students【23†source】.
- Automated Lecture Transcription and Subtitling: AI can transcribe lectures in real time, making content more accessible for students and generating study materials【28†source】.
- Predictive Analysis in Student Success: AI can identify students who may be at risk of failing or dropping out, enabling early interventions【28†source】.
- Enhanced Learning through Virtual Reality: AI can create immersive learning experiences in virtual reality environments【28†source】.
- Assistance in Coding and Programming: AI can provide real-time assistance as students write code, enhancing the learning experience【28†source】.
- Interactive AI Tutors: Intelligent tutoring systems use AI to provide personalized instruction to students, adapting to a student’s learning style and pace【28†source】.
- Research Paper Drafting: AI can generate a draft of a scientific paper based on the data and notes provided【23†source】.
- Data Analysis: AI can analyze data and identify patterns, saving researchers time compared to traditional data analysis methods【23†source】.
- Simulations and Modeling: AI can create complex simulations and models, aiding research in fields like physics, biology, and economics【23†source】.
- Literature Search and Review: AI can expedite the process of literature search and review by summarizing multiple articles【23†source】.
- Synthetic Data Generation: AI can create synthetic data for preliminary analysis, hypothesis testing, or model training【28†source】.
- Automated Statistical Analysis: AI can perform complex statistical analyses on research data, generating visualizations, conducting hypothesis testing, and more【34†source】.
- Document Summarization: AI can automatically summarize long documents, useful for reviewing a large number of papers【34†source】.
- Semantic Search: AI can provide more relevant search results based on the searcher’s intent, making it easier to find information【34†source】.
- AI in History and Archaeology: AI can analyze historical texts, artifacts, or archaeological sites, aiding researchers in their studies【34†source】.
- Automated Language Translation: AI can automatically translate academic papers, lectures, or other educational materials, making them accessible to international students or researchers【28†source】.
- Plagiarism Detection: AI can compare a student’s work against a vast database to detect possible plagiarism, ensuring academic integrity【23†source】.
- Automated Citation and Bibliography Generation: AI can automate the process of citation and bibliography creation, a task that can be time-consuming and prone to errors【28†source】.
- Facial Recognition for Attendance: AI can automate attendance taking in large lectures, saving time for professors and ensuring a more accurate record【34†source】.
- Automated Proofreading: AI can detect grammar, punctuation, and spelling errors in a text, a valuable tool for writing papers, theses, or dissertations【34†source】.
This is what it eventually came up with for public engagement:
- AI Education and Outreach: Initiatives like MIT’s Responsible AI for Social Empowerment and Education (RAISE) promote understanding and use of AI across society, with an emphasis on equity, access, and responsible empowerment. They engage in research, education, and outreach activities to advance AI literacy, diversity, and inclusion in AI, AI workforce training, and AI-supported learning123.
- AI-Augmented Learning Tools: RAISE is developing personalized learning companion AI agents such as social robots to support individual students’ learning and motivation. This work provides an effective and safe space for students to practice and explore topics such as early childhood literacy and language development4.
- K-12 Outreach Programs: Programs like Future Makers offer hands-on learning experiences about AI fundamentals and societal implications to teachers and students, primarily from underserved or under-resourced communities, such as schools receiving Title I services5.
- Curriculum Development and Distribution: Initiatives like RAISE develop and distribute curricula, teacher guides, and student-friendly AI tools that enable anyone, even those with no programming background, to create original applications for desktop and mobile computing. This approach aims to make powerful AI accessible to all people for increased creativity and personal expression6.
One response to “📍Top AI tools for knowledge work”
great resource – enjoy your site very much !