100 AI use cases • Executive briefs • Technical analysis
Imagine a huge classroom where different versions of Google’s Gemini sit side‑by‑side answering the same homework and exam questions. A panel of judges then scores which Gemini answers are most helpful for students. This paper is about building that classroom arena and seeing how good Gemini really is as a learning assistant.
This is like having two different “weather apps” for grades. Both look at a student’s past behavior and background (attendance, homework, test scores, etc.) and try to forecast how well they will do in the future. The paper compares which forecasting engine—XGBoost or Random Forest—does a better job at predicting students’ academic performance.
This uses GPT-4 as an always-on assistant teacher that reads students’ short-answer responses and suggests grades the way a human marker would, based on a rubric or example answers.
Think of a future MBA program that behaves more like Netflix and Duolingo combined: it recommends the right courses, adapts in real time to each learner, uses AI tutors instead of TAs for basic questions, and plugs into real company data and tools instead of static textbooks.
Imagine every student getting a 24/7 teaching assistant who knows their strengths, weaknesses, and pace, and quietly adjusts homework, hints, and explanations just for them. This Dartmouth work shows that AI can realistically play that role for large classes at once.
This is like having a smart digital tutor that learns how each student studies best, then automatically adjusts lessons, examples, and practice questions to fit that student—while helping teachers design and manage this at scale.
This is like giving every student a personal Netflix for learning: as they study, an AI quietly watches what they’re doing and how they’re performing, then instantly suggests the next best video, article, or quiz question to keep them learning at the right level.
Think of this as turning tools like ChatGPT into a smart study and research partner for a university: it helps students learn faster, teachers design better lessons, and researchers explore ideas more quickly, all while the university figures out how to use it safely and effectively.
This is like giving every student their own patient, always-available tutor that knows the curriculum, their past performance, and how they like to learn, and then adapting lessons, practice questions, and explanations just for them in real time.
This is like a smart early‑warning system for universities: it looks at patterns in student data (grades, attendance, demographics, behavior on learning platforms) and predicts which students are likely to struggle or drop out so staff can intervene earlier.
This is like a smart early‑warning system for online classes: it watches how students learn on the platform (logins, quiz scores, time spent, etc.) and predicts who is likely to struggle or drop out so teachers can intervene early.
This is like a smart sorting hat for online classes: it looks at student data and predicts how well each student is likely to adapt to online learning, so instructors and schools can give extra help to those who might struggle.
This is like having a smart early-warning system for students: it quietly watches patterns in their grades, attendance, and engagement, and then flags which students are most at risk of failing or dropping out so staff can step in sooner.
This is like building a detailed ‘digital twin’ of each student that learns what they know, how they learn, and where they struggle, so any learning system (LMS, tutoring app, or classroom tool) can adapt content and feedback to them automatically.
This is like giving every student their own smart tutor that learns how they learn, adjusts lessons and exercises to their pace, and gives teachers a dashboard to see who needs what help—automatically.
This is Google’s push to put AI ‘co‑pilots’ into classrooms and homework tools, so students and teachers can get personalized help, smarter search, and automated support directly inside Google’s existing education products (like Search, Chrome, Workspace, and Classroom).
This is like hiring a panel of different “tutors” (machine learning models) and then using a smart coach to decide how much to listen to each tutor so you get the best possible prediction about how a student will perform. Optuna is an automated assistant that keeps tweaking the settings of this coach until the whole team gives the most accurate answers.
This is like giving every teacher a super-fast, tireless teaching assistant that can read student work, score it, and draft feedback so the teacher can focus on teaching instead of paperwork.
Imagine every student and every teacher having a patient, always-available tutor in their laptop that knows the Khan Academy curriculum and can explain things step by step, ask questions back, and guide practice instead of just giving answers. That’s what Khanmigo is: an AI helper built into Khan Academy for learning and teaching.
Imagine every exam and assignment at a university having a tireless digital assistant that helps design fair questions, checks grading for consistency, and clearly explains to students why they got the grade they did. That’s what this kind of AI does for assessments.
This is like giving every college student a 24/7 smart study coach that can explain concepts in simple terms, quiz them, and help them plan their learning, rather than just giving them another digital textbook.
Think of a pile of student essays. Instead of teachers grading every essay one by one with a long rubric, the system just keeps asking: ‘Which of these two is better?’ After lots of these quick comparisons, the software works out a reliable score for every piece of work. It’s like ranking players in a tournament, but for writing and exams.
Think of this as a super-smart teaching assistant that can instantly create practice questions, explain hard concepts in simpler words, draft lesson plans, and give students personalized feedback 24/7.
This is basically a playbook for teachers on how to use tools like ChatGPT in smart, creative ways—like having a tireless teaching assistant who helps write lessons, examples, and exercises, while students also learn how to use AI critically and responsibly.
This is like giving every student a smart digital coach that adapts to how they learn, keeps them engaged while they practice, and quietly tracks their progress so teachers can step in at the right time.
Think of this as an early‑warning radar for student success. It looks at students’ past grades, attendance, and other records and then predicts who is likely to do well or struggle, so teachers and administrators can step in before problems become failures.
This is like giving doctors a super-powered microscope and calculator that look at thousands of biological signals at once (genes, proteins, metabolites, etc.) to find patterns in ME/CFS patients, so treatments can be tailored to each person instead of using one-size-fits-all guesses.
Imagine every learner having a personal tutor who watches how they learn, what they get right or wrong, how fast they move, and then quietly rearranges the course so they only see what they need next. That’s adaptive learning inside an LMS: the course reshapes itself in real time for each person.
This is like giving every learner their own smart digital tutor that automatically adjusts lessons, exercises, and assessments in real time—based on what the learner already knows, how they respond, and how fast they progress—by coordinating several AI “helper bots” behind the scenes.
Imagine every student having a patient, expert tutor who is available 24/7, remembers what they know, explains things in many ways, and can instantly create new practice problems and feedback—powered by ChatGPT‑like technology instead of a human.
This is like having an always-available teaching assistant that reads students’ short answers and reports, compares them to a grading guide, and suggests scores and feedback so instructors don’t have to grade everything by hand.
Think of modern AI in schools as a super-smart homework helper and writing coach that students can use at any time. It can draft essays, solve math problems, and explain concepts in plain language—sometimes so well that it’s hard to tell what work is the student’s and what work is the AI’s.
This is a playbook for statisticians on how to use advanced machine learning safely when answering questions like “Does this drug really reduce the risk of death or relapse over time?” It combines causal inference math with survival analysis so that researchers can get more reliable answers from complex clinical data without fooling themselves.
This is about using very smart pattern-recognition software to help scientists find new medicines faster. Instead of testing every possible molecule in a lab, deep learning models "imagine" which molecules are most likely to work and be safe, so researchers only test the best candidates in real life.
This is like an early‑warning system for students: it looks at past grades, attendance, and other academic data to predict which students are likely to struggle, so staff can step in and help before they actually fail.
This work is like testing whether a student-success prediction tool that works for one class or group of students will still work well for a different class or a different course, and under what conditions it breaks down.
Think of AI in education as a smart assistant for schools: it helps teachers grade faster, suggests personalized practice for each student like a custom tutor, and keeps track of who needs help and where.
This work is like putting a ‘Fitbit’ on a blended learning master’s program: it tracks different aspects of students’ experience (online and in-person) and then uses data analytics to see which factors are most strongly linked to their grades.
This is like an “early warning radar” for schools: it looks at student data and predicts which students are at risk of dropping out, while also explaining in plain terms why it thinks so (e.g., poor attendance, grades trend, engagement).
This is like giving teachers a smart early-warning radar: it looks at patterns in students’ data (grades, attendance, behavior, etc.) and predicts which students are likely to struggle or succeed, so schools can step in early with support.
Think of these AI learning tools as a smart teaching assistant that sits beside each student, explains concepts in different ways, gives instant practice questions, and adapts to how fast or slow the student is learning.
This is like a smart early‑warning system for universities: it studies past students’ data to learn which patterns usually lead to dropping out, then flags at‑risk students early so staff can step in with support before it’s too late.
Think of this as an automated math tutor that watches how a student answers questions, figures out exactly what level of understanding they are at, and then routes them onto the best next learning path instead of giving the same fixed lesson to everyone.
Think of a smart digital tutor that adapts to each student like a great human teacher would—but without ever exposing the student’s sensitive data. The system learns what works for each learner while keeping their information locked down and, where possible, processed locally or in heavily protected form.
This is like giving every student their own smart coach that reads what they write about their learning, gives personalized feedback, and does it in a way that’s fair across different backgrounds and ability levels—all powered by AI ‘feedback agents’ playing specific roles (e.g., grader, mentor, peer).
Think of this as a smart teaching assistant that sits inside a virtual classroom. It watches how each student learns, adapts lesson difficulty and content to their pace, answers questions instantly, and helps teachers manage and monitor the class more efficiently.
This is like giving every student their own smart tutor who can explain topics in different ways, generate practice questions, and adapt to how fast they learn and what they struggle with—automatically and at scale.
Imagine every student getting a custom textbook and practice set that rewrites itself for their level, interests, and progress—generated on demand by an AI ‘teacher’s assistant’ instead of one-size-fits-all materials.
This looks like a data science project where different AI/ML models are being compared to see which predicts best for an education-related outcome (for example, student success or course performance). Think of it as a “bake-off” between algorithms to pick the most accurate one for a school-related prediction task.
This is like running a competition between different “prediction robots” to see which one is best at answering a specific education question, such as who might pass a course, drop out, or need extra support. The paper compares several robots (machine‑learning classifiers) on the same student data and measures who does the job best and most consistently.
This is like a very smart calculator built from real patient histories that estimates how long a HER2-positive early-stage breast cancer patient is likely to live under different treatment options, so doctors and drug developers can see which approaches tend to work best for which patients.
This is like an early-warning radar for a classroom: it watches students’ activity in an online–offline (blended) course and predicts which students are likely to do well or poorly, so teachers can step in before final grades are set.
This is like an early‑warning system for schools that looks at student records and quietly tells staff, “These 50 students are at high risk of dropping out—pay attention to them now.”
Think of this as a data-driven early‑warning system for student performance. It watches how students study and interact with learning systems (attendance, homework, online activity, quiz results), then uses a prediction model to estimate who is likely to struggle or succeed so teachers can intervene early.
This research is like having a smart assistant look at years of student records and tell you, “These are the few things that really matter for whether bioengineering students actually master the skills you care about.” It tests different machine learning models on student data to discover which factors (attendance, prior grades, course activities, etc.) best predict whether students will achieve required competencies.
Think of this as a menu of ways schools can use AI as a smart helper: it can tutor students one‑on‑one, grade homework, customize lessons, and keep an eye on who’s falling behind—so teachers can focus on real teaching instead of admin busywork.
Think of this as an AI teaching assistant that can read students’ short written answers (a few sentences) and score them like a human grader would, using examples of past student answers and grades to learn what ‘good’ and ‘bad’ look like.
This is like a Lego kit of online test and quiz tools that schools and education companies can plug into their own platforms, now with AI features to help create, deliver, and grade assessments more easily.
Think of this as putting a very smart calculator that can also read and write into a first‑year physics class and asking: could it do the homework and pass the exams like a human student? The study systematically checks how far today’s AI can go in a real physics course, not just on toy examples.
This is like building a prediction engine that looks at many signals about a student (attendance, past grades, assignments, demographics, etc.) and estimates how well they’re likely to perform in the future, so educators can help earlier instead of waiting for exam results.
Imagine every student getting their own super-smart, always-available digital tutor—like the talking book in Neal Stephenson’s ‘The Diamond Age’—that adapts lessons, stories, and exercises in real time to how that specific child learns.
This is like building several different “grade prediction calculators” for students, then comparing which one is best at forecasting who will do well or poorly so schools can intervene early.
This is like having a tireless teaching assistant that can grade student work quickly and consistently, but always keeps a human teacher in charge to review and adjust the grades before they’re final.
This is like turning a traditional online classroom (LMS) into a smart teaching assistant that can understand what each student is doing, recommend content, and help teachers manage and personalize learning automatically.
Think of this as a smart teaching assistant that watches how each child learns, what they struggle with, and then quietly adjusts the lessons, pace, and practice questions so every student gets a custom-fit learning path—like a personal tutor for every child, running in the background of their school tools.
This is about using AI to act like a smart private tutor inside online learning platforms—adapting lessons, exercises, and feedback to each student’s pace, knowledge gaps, and preferences instead of giving everyone the same generic course.
Imagine your school’s IT systems rebuilt so every app, homework tool, and chatbot can safely talk to each other and to AI—like reorganizing the whole campus library, gradebook, and learning apps so a smart assistant can help every student and teacher personally, without losing track of who is allowed to see what.
This is like giving teachers a weather forecast for each student’s grades. By looking at past test scores, attendance, and study habits, AI models estimate which students are likely to do well or struggle so schools can intervene earlier.
This is like an early‑warning system for student grades: it uses past student data (attendance, assignments, prior scores, demographics, etc.) to predict whether a student is likely to get an A, B, C, or fail, so educators can intervene sooner.
This is like an early-warning radar for schools: it uses past data about students and teachers (attendance, grades, evaluations, etc.) and runs several math-based prediction methods to see who might excel or struggle, so interventions can happen sooner.
Think of this as a smart digital teaching assistant that can explain topics, quiz students, and adapt to how each learner is doing, instead of giving everyone the same textbook and homework.
Imagine every student having a tireless, smart tutor that adapts to how they learn, checks their work instantly, and suggests the best next exercise—available on any device, anytime. This paper describes how AI systems can do that at scale for schools and universities.
Imagine every teacher having a super-fast teaching assistant that can read students’ homework and tests, score them instantly, and point out where each student is struggling, while the teacher focuses on teaching and coaching instead of marking piles of papers.
This is like giving every student their own smart teaching assistant that adjusts lessons, practice, and feedback to how they learn, while also giving the teacher a co-pilot that helps design materials, explain concepts differently, and track who needs what.
This is like an app store and lab for AI tools built specifically for teaching and learning—things that help write lessons, tutor students, grade work, and create educational content using generative AI.
This is like having an AI ‘teaching assistant’ quietly watching how students interact with digital lessons—how often they log in, what they click, how long they stay focused—and then turning that into a clear picture of who is engaged, who is struggling, and which activities actually work best.
Flexynesis is like a master translator that takes many different “languages” of biological data (DNA, RNA, proteins, etc.) from cancer patients and turns them into one coherent story that computers can learn from. This makes it easier to discover which patients might benefit from which drugs, and to find new disease patterns that humans would miss.
This is about giving language teachers a smart assistant that learns what each student needs and then helps create tailored exercises, feedback, and practice activities for them—like having a co‑teacher that never gets tired of differentiating instruction.
Think of this as a smart digital teaching assistant for every student. It watches how each child learns, what they struggle with, and what they’re good at, then adjusts lessons, practice questions, and feedback so each student gets a “just right” learning path—like every kid having their own tutor that never gets tired.
Think of AI in education as a smart teaching assistant that helps every child learn at their own pace, explains things in different ways when they’re stuck, and takes over routine tasks so teachers can focus on actual teaching and mentoring.
Think of a smart dashboard for a school that turns all the numbers about students’ grades, attendance, and activities into easy-to-read charts, so teachers and administrators can quickly spot which students might be falling behind and help them earlier.
This paper is like a buyer’s guide for how to analyze whether a new drug works in clinical trials, comparing traditional statistics with newer AI and machine‑learning methods.
This is like an early-warning radar for schools: it looks at students’ past grades, attendance, and other factors to predict who is likely to do well or struggle, so teachers can step in before problems become failures.
This is like an early-warning radar for a college physics class. It looks at students’ past grades and course activity with machine learning and predicts who is likely to struggle or fail, so instructors and advisors can step in sooner.
Think of this as a very fast teaching assistant that can read students’ answers and assign scores automatically, instead of a human teacher marking everything by hand.
This work is about teaching computers to ‘fold’ and ‘design’ proteins in silico. Think of it as a super–smart origami assistant that can look at a string of amino acids and predict the 3D shape it will fold into – or even suggest brand‑new strings that will fold into shapes we want for new drugs or enzymes.
This is like teaching an AI-powered 3D puzzle master to more accurately figure out how short protein fragments (peptides) stick to larger proteins, by letting it reason about the 3D shape and connectivity of the molecules rather than just their sequences.
This is like a smart guidance counselor that studies many past students’ school records and behaviors, then uses a very advanced pattern-recognizing calculator (a transformer model) to predict which students are likely to succeed in their careers and why.
This is like an AI ‘teaching assistant’ that looks at many past students’ data, learns patterns about who is likely to pass or struggle, and explains in simple terms which factors (attendance, prior grades, study habits, etc.) matter most—so teachers and administrators can intervene early.
Think of this as a ‘mood-aware’ layer for digital learning tools: software and AI that try to sense students’ emotions (e.g., confusion, boredom, engagement) and then adapt teaching content or support accordingly. This paper doesn’t sell a product; it summarizes and quantifies what’s been tried so far, how well it works, and where it’s still shaky.
Imagine running many clinical trials in slightly different patient groups and diseases, and wanting to learn the best treatment choice for each individual patient. This paper proposes a way to carefully “borrow strength” from related trials without blindly pooling everything together, so that past data helps but doesn’t overpower what’s truly different in a new setting.
This is like giving every teacher a smart assistant that reads all the early signals from a course (attendance, assignments, online activity, etc.) and then predicts which students are likely to struggle later, so support can be offered before they actually start failing.
This is about using smart computers to read and understand massive DNA and RNA data from next‑generation sequencing, the way a super‑powered spell‑checker reads and compares millions of books at once to spot tiny differences and patterns that humans would miss.
Think of OpenAI Academy as an online school that teaches people and teams how to use tools like ChatGPT in their work, using structured lessons and examples instead of random YouTube videos.
This is a policy and practice guide for teachers on what to do when a student turns in homework written by an AI tool like ChatGPT and claims it as their own.
Think of Learn Buddy as a smart digital study partner for students: it uses generative AI to answer questions, explain concepts in simple terms, and adapt to how each student learns—like a personal tutor that’s always available on their device.
This work is like a road test and safety inspection for AI tools that grade or review student essays. It checks how accurate, fast, and fair they are compared with human graders.
This is like giving teachers a digital production studio that can quickly help them create narrated lessons, simple animations, and podcasts using AI, so they can focus more on teaching and less on editing and production work.
This is a scholarly book that explains how AI tools and teaching methods come together in schools and universities—like a playbook for using smart software to support learning, teachers, and education systems.
This is a teacher-focused guide (developed with Apple) that explains what AI is, how it works, and how to use it safely and effectively in the classroom—more like a curriculum and set of best practices than a single AI app.