MIT RAISE (Responsible AI for Social Empowerment and Education)https://hdl.handle.net/1721.1/1628232026-04-04T21:29:06Z2026-04-04T21:29:06ZAI for Community: A Student-Led Initiative Promoting Sustainability Awareness Through App Development and Community EngagementWang, JustinTang, Justinhttps://hdl.handle.net/1721.1/1631492025-10-11T06:55:18Z2025-07-01T00:00:00ZAI for Community: A Student-Led Initiative Promoting Sustainability Awareness Through App Development and Community Engagement
Wang, Justin; Tang, Justin
This paper presents AI for Community, a student-led initiative where high school students develop AI-powered solutions for sustainability. Starting with a biodiversity-focused Native Plant Awareness app, the initiative demonstrates the impactful intersection of technological innovation and environmental conservation. Building on this foundation, the initiative has expanded to address other sustainability challenges—such as ocean conservation and senior care, demonstrating how AI can drive both environmental and social impact.
2025-07-01T00:00:00ZAI Generation – an AI Literacy curriculum for disadvantaged youth in RomaniaUiPath Foundationhttps://hdl.handle.net/1721.1/1631482025-10-11T06:55:38Z2025-07-01T00:00:00ZAI Generation – an AI Literacy curriculum for disadvantaged youth in Romania
UiPath Foundation
We believe technology is the key to unlocking educational opportunities for children in vulnerable communities, when thoughtfully integrated with the realities they face. By providing essential digital infrastructure such as tablets, laptops and reliable internet access, an online learning platform, we open doors to boundless learning possibilities. We equip children with essential programming and AI skills as a core part of their digital education. By mastering these technologies, they gain the tools needed to thrive in the future workforce.
2025-07-01T00:00:00ZEvaluating the Spatial Reasoning Capabilities of Large Multimodal Models on Chest X-Ray Anomaly DetectionLi, Linday Skylarhttps://hdl.handle.net/1721.1/1631472025-10-11T06:55:28Z2025-07-01T00:00:00ZEvaluating the Spatial Reasoning Capabilities of Large Multimodal Models on Chest X-Ray Anomaly Detection
Li, Linday Skylar
While current results show potential in LMM-based diagnosis, it is unclear if the output of them are backed by strong spatial reasoning capabilities. To evaluate this, I provided GPT-4o with chest X-rays and asked it to return diagnoses and the coordinates of bounding boxes that surrounded any identified abnormalities on the NIH chest X-ray dataset. I find variable performance across different images in the dataset, suggesting the need for further development of the spatial reasoning capabilities of LMMs.
2025-07-01T00:00:00ZDemocratizing Biotech: How AI-Powered Virtual Labs Could Transform Global Biotechnology LearningKumar, Aaravhttps://hdl.handle.net/1721.1/1631462025-10-11T06:55:40Z2025-07-01T00:00:00ZDemocratizing Biotech: How AI-Powered Virtual Labs Could Transform Global Biotechnology Learning
Kumar, Aarav
This paper examines how AI-powered virtual labs can democratize biotechnology education, enabling students in even the most remote areas to conduct sophisticated experiments.
2025-07-01T00:00:00ZMindset Math × Data Science: A Formula for a Multidisciplinary Framework in Math InstructionSenajon, Samantha ClarisseNethikunta, Sanvihttps://hdl.handle.net/1721.1/1631452025-10-11T06:55:23Z2025-07-01T00:00:00ZMindset Math × Data Science: A Formula for a Multidisciplinary Framework in Math Instruction
Senajon, Samantha Clarisse; Nethikunta, Sanvi
Mindset Math introduces an initiative based on a hybrid model of data science and traditional educational algebraic curricula built upon the success of previous projects. Highlighting the diverse use of Artificial Intelligence (AI) in career and technical fields, Mindset Math aims to use data science’s multidisciplinary properties to supplement the growth of data literacy and quantitative analysis abilities.”
2025-07-01T00:00:00ZMulticultural Education with AI: The Case of “A World at the Table”Peluso, Anna Luciahttps://hdl.handle.net/1721.1/1631442025-10-11T06:55:31Z2025-07-01T00:00:00ZMulticultural Education with AI: The Case of “A World at the Table”
Peluso, Anna Lucia
The ‘A World at the Table’ project describes an interdisciplinary teaching experience conducted in a multicultural lower secondary school class at the IC “Ferrajolo – Siani” in Acerra (NA). Its aim was to promote integration, inclusion, and global citizenship through the collaborative writing of a song. Using digital tools and generative Artificial Intelligence (GenAI), students from diverse cultural backgrounds (Albania, Belarus, Brazil, Italy, Serbia, Morocco, Ukraine) co-created song lyrics that reflect the value of diversity, inclusion, and intercultural dialogue, inspired by traditional dishes from their home countries.
2025-07-01T00:00:00ZAI on AI: Can GenAI Tools Design and Evaluate Course Outlines Better Than We Think?Kumar, Jeya Amanthahttps://hdl.handle.net/1721.1/1631432025-10-11T06:55:41Z2025-07-01T00:00:00ZAI on AI: Can GenAI Tools Design and Evaluate Course Outlines Better Than We Think?
Kumar, Jeya Amantha
Despite the increasing use of generative AI (GenAI) tools in education, little is known about their effectiveness in producing pedagogically sound instructional materials. Therefore, this study evaluated the performance of six GenAI tools as instructional designers in generating a unit or module outline for an undergraduate course, focusing on developing learning objectives based on Universal Design for Learning (UDL) principles and later evaluating each outcome.
2025-07-01T00:00:00ZSwype AI: A Multimodal Voice and Gesture Control System for Accessible EducationGaneshkumar, Dhanvinkumarhttps://hdl.handle.net/1721.1/1631422025-10-11T06:55:34Z2025-07-01T00:00:00ZSwype AI: A Multimodal Voice and Gesture Control System for Accessible Education
Ganeshkumar, Dhanvinkumar
Swype AI [uses] a real-time software system that combines natural voice and gesture control to replace traditional peripherals. It runs on consumer laptops without requiring specialized hardware.
2025-07-01T00:00:00ZTransplant waiting list: Technology transforming livesFerraz, Carolina Lima DuarteRamirez, Julia Beltrão Lemoshttps://hdl.handle.net/1721.1/1631412025-10-11T06:55:29Z2025-07-01T00:00:00ZTransplant waiting list: Technology transforming lives
Ferraz, Carolina Lima Duarte; Ramirez, Julia Beltrão Lemos
Although Brazil is a world reference in organ transplants, having the largest public transplant system in the world, the shortage of organs is still a worrying scenario in the country, since the number of effective donors does not meet the demand for transplants (Brasil, 2024). Thus, the work aims to investigate and understand the waiting list model in the organ transplant process, in such a way that it is possible to mitigate this scenario through the development of an app.
2025-07-01T00:00:00ZHow Policy Can Help Ensure the Proper Use of AI in K-12 EducationDiPaola, DaniellaSalazar-Gómez, Andrés F.Abelson, HalKlopfer, EricGoldston, DavidBreazeal, Cynthiahttps://hdl.handle.net/1721.1/1631402025-10-11T06:55:38Z2024-07-19T00:00:00ZHow Policy Can Help Ensure the Proper Use of AI in K-12 Education
DiPaola, Daniella; Salazar-Gómez, Andrés F.; Abelson, Hal; Klopfer, Eric; Goldston, David; Breazeal, Cynthia
2024-07-19T00:00:00ZAssessing AI Characters as Facilitators of Children’s Learning ExperiencesTiwari, Soniahttps://hdl.handle.net/1721.1/1631392025-10-11T06:55:33Z2025-07-01T00:00:00ZAssessing AI Characters as Facilitators of Children’s Learning Experiences
Tiwari, Sonia
This study examines how children’s interaction with AI characters can shape their learning experiences. Drawing on a literature review of child–AI interactions in educational contexts, this study presents AI Character Assessment (AIC-A) as an analytical framework for researchers assessing how well an AI character’s design and interaction align with children’s needs.
2025-07-01T00:00:00ZLocalized Intelligence: Designing an AI-Enhanced OER Course for Faculty Development in a Low-Resource Language ContextShilibekova, Aigerimhttps://hdl.handle.net/1721.1/1631382025-10-11T06:55:30Z2025-07-01T00:00:00ZLocalized Intelligence: Designing an AI-Enhanced OER Course for Faculty Development in a Low-Resource Language Context
Shilibekova, Aigerim
The paper introduces the concept of localized intelligence, a pedagogical principle that frames instructional design with AI as a situated and context-responsive practice, guided by human expertise. This approach challenges assumptions of AI scalability and oZers a replicable model for designing inclusive, culturally aligned professional learning experiences, with implications for multilingual faculty development across global contexts.
2025-07-01T00:00:00ZReturn of the Jibo: Generative AI & Social Robots for Virtual Production EducationPillis, D.Ferguson, Jonhttps://hdl.handle.net/1721.1/1631372025-10-11T06:55:40Z2025-07-01T00:00:00ZReturn of the Jibo: Generative AI & Social Robots for Virtual Production Education
Pillis, D.; Ferguson, Jon
This paper discusses the use of Jibo, a socially intelligent robot developed at MIT, paired with the introduction of generative AI, as a method to introduce AI in educational settings, specifically, their combined integration into film-making pedagogy at a film school.
2025-07-01T00:00:00ZPersonalized Reading and Writing Tutor: Improving Literacy Skills and Assessment AccuracyKopikar, MokshMandloi, Namanhttps://hdl.handle.net/1721.1/1631362025-10-11T06:55:30Z2025-07-01T00:00:00ZPersonalized Reading and Writing Tutor: Improving Literacy Skills and Assessment Accuracy
Kopikar, Moksh; Mandloi, Naman
This paper presents the development and evaluation of the Reading Writing Tutor (RWT), a personalized learning assistant powered by Large Language Models (LLMs), designed to enhance students’ reading and writing skills according to state education standards.
2025-07-01T00:00:00ZReflections from UK and US Classrooms on Building Responsible AI LiteracyWang, Justinia J.https://hdl.handle.net/1721.1/1631352025-10-11T06:55:25Z2025-07-01T00:00:00ZReflections from UK and US Classrooms on Building Responsible AI Literacy
Wang, Justinia J.
This position paper highlights the importance of fostering responsible AI literacy in secondary education, drawing on personal observations from contrasting technology-use environments in UK and US schools. The author critiques simplistic regulatory approaches, emphasizing that unchecked AI use can amplify misinformation, limit student creativity, and impair critical thinking. The paper advocates for comprehensive AI education through curriculum enhancements and targeted training. Such measures aim to help students un-derstand AI’s capabilities, limitations, and ethical implications, and to encourage informed, balanced engagement with the technology.
2025-07-01T00:00:00ZLexington High School AI in Education Policy: A ProposalTang, Ryanhttps://hdl.handle.net/1721.1/1631342025-10-11T06:55:43Z2025-07-01T00:00:00ZLexington High School AI in Education Policy: A Proposal
Tang, Ryan
This AI policy includes guidance on appropriate usage of genAI, inappropriate usage of genAI, consequences of inappropriately using genAI, how to cite genAI, and data privacy. Currently, Lexington High School in Massachusetts lacks a clear policy on genAI for students. This policy fills the gap to encourage consistent application of usage and discipline concerning genAI in education. Overall, the goal of this policy is to promote student-teacher understanding of how to use genAI appropriately as an educational tool in the classroom.
2025-07-01T00:00:00ZTeaching AI with Humanity: Breaking Barriers. A Journey into Ethical and Inclusive Artificial Intelligence through Hands-On, Student-Centered LearningPieraccini, Danielahttps://hdl.handle.net/1721.1/1631332025-10-11T06:55:26Z2025-07-01T00:00:00ZTeaching AI with Humanity: Breaking Barriers. A Journey into Ethical and Inclusive Artificial Intelligence through Hands-On, Student-Centered Learning
Pieraccini, Daniela
By integrating supervised machine learning, block-based programming, and inclusive design, the initiative empowered students to understand, criticize, and apply AI with ethical sensitivity, and practical relevance.
2025-07-01T00:00:00ZFuture architecture: Use of Artificial Intelligence in sustainable construction projectsLeitão, André CunhaAmaral, Carl Erhard DolderHenriques, Pedro Laudisiohttps://hdl.handle.net/1721.1/1631322025-10-11T06:55:22Z2025-07-01T00:00:00ZFuture architecture: Use of Artificial Intelligence in sustainable construction projects
Leitão, André Cunha; Amaral, Carl Erhard Dolder; Henriques, Pedro Laudisio
The development of an App allowed the experience of using an innovative and digital solution with sustainability, as well as the confirmation that the main contribution of the study is to help society with actions to minimize damage to the environment.
2025-07-01T00:00:00ZSocratic AI Tutoring in Primary School Mathematics: A Case Study on the Development of Problem-Solving and Digital Competence According to DigComp 2.2Avella, Barbarahttps://hdl.handle.net/1721.1/1631312025-10-11T06:55:24Z2025-07-01T00:00:00ZSocratic AI Tutoring in Primary School Mathematics: A Case Study on the Development of Problem-Solving and Digital Competence According to DigComp 2.2
Avella, Barbara
This study investigates the effectiveness of a digital Socratic tutoring approach in enhancing mathematical problem-solving skills in primary school students, using the European DigComp 2.2 framework as a reference. Through a qualitative case study conducted in a fifth-grade classroom, the interaction between students and an AI tutor during math activities was analyzed. The preliminary results are promising. They show that Socratic questioning fosters both mathematical and digital competencies, aligning with recent research on AI-mediated reflective learning.
2025-07-01T00:00:00ZBINGO!: A Novel Neural Network Pruning Mechanism to Allow For Physical Computing in AI EducationPanangat, Adityahttps://hdl.handle.net/1721.1/1631302025-10-11T06:55:32Z2025-07-01T00:00:00ZBINGO!: A Novel Neural Network Pruning Mechanism to Allow For Physical Computing in AI Education
Panangat, Aditya
BINGO, during the training pass, studies specific subsets of a neural network one at a time to gauge how significant of a role each weight plays in contributing to a network’s accuracy. By the time training is done, BINGO generates a significance score for each weight, allowing for insignificant weights to be pruned in one shot. BINGO provides an accuracy-preserving pruning technique that is less computationally intensive than current methods, allowing for a world where students can learn about AI through engaging physical computing activities.
2025-07-01T00:00:00ZA Markov Chain Tool for Grade 6-12 Learners to Explore Generative AIEllis, RebeccaChao, JieRosé, CarolynJiang, Shiyanhttps://hdl.handle.net/1721.1/1631292025-10-11T06:55:27Z2025-07-01T00:00:00ZA Markov Chain Tool for Grade 6-12 Learners to Explore Generative AI
Ellis, Rebecca; Chao, Jie; Rosé, Carolyn; Jiang, Shiyan
The AI Education Across the Curriculum Project (also known as StoryQII) has developed a digital tool to support students to represent, inspect, and generate text using a Markov chain. The tool is designed for use in an English Language Arts (ELA) class and does not require coding or statistics. Using this tool and its accompanying curriculum module, secondary students learn the basics of text generation and how it relates to the core ELA concepts of voice, authorship, and creativity. This tool has been tested in ninth grade, eleventh, and twelfth grade ELA classes with promising results for teaching students about generative AI.
2025-07-01T00:00:00ZThe Impact of Generative AI on Middle and High School Students’ Willingness to Engage with Teachers in ClassCai, Rileyhttps://hdl.handle.net/1721.1/1631282025-10-11T06:55:42Z2025-07-01T00:00:00ZThe Impact of Generative AI on Middle and High School Students’ Willingness to Engage with Teachers in Class
Cai, Riley
This paper focuses on whether preparing questions with generative AI increases students ‘ willingness to ask questions in class. […] Results indicate AI functions as a pre-questioning helper that reduces anxiety and strengthens student-teacher interaction rather than replacing it.
2025-07-01T00:00:00ZGreenMiles: Utilizing Deep Learning to Analyze Vehicular Carbon Emission TrendsArunkumar, Rohanhttps://hdl.handle.net/1721.1/1631272025-10-11T06:55:33Z2025-07-01T00:00:00ZGreenMiles: Utilizing Deep Learning to Analyze Vehicular Carbon Emission Trends
Arunkumar, Rohan
I developed a set of deep learning models that analyze patterns in vehicle-related carbon emissions using an official dataset from the Canadian government. The models identified which vehicle settings (such as fuel type and transmission) are most strongly associated with high emissions. After testing, the best-performing model was deployed on a user-friendly web application, where consumers can input different vehicle parameters and receive predicted CO₂ emission levels
2025-07-01T00:00:00ZOpportunities, Issues, and Challenges for Generative AI in Fostering Equitable Pathways in Computing EducationBreazeal, CynthiaRai, ArunRamesh, BalasubramaniamChen, LiweiLong, YuanAria, AndreaLoi, HaoTorralba, AntonioBernstein, JeremyReich, JustinKlopfer, EricAbelson, HalWesterman, GeorgeBosch, Christinahttps://hdl.handle.net/1721.1/1631262025-10-10T12:36:12Z2024-08-28T00:00:00ZOpportunities, Issues, and Challenges for Generative AI in Fostering Equitable Pathways in Computing Education
Breazeal, Cynthia; Rai, Arun; Ramesh, Balasubramaniam; Chen, Liwei; Long, Yuan; Aria, Andrea; Loi, Hao; Torralba, Antonio; Bernstein, Jeremy; Reich, Justin; Klopfer, Eric; Abelson, Hal; Westerman, George; Bosch, Christina
The objective of this whitepaper is to identify opportunities, issues, and challenges facing equitable education pathways for careers in computing and the particular role that generative artificial intelligence (AI) could play to support postsecondary education at minority-serving institutions (MSIs) and community colleges (CCs).
2024-08-28T00:00:00ZListening with Language Models: Using LLMs to Collect and Interpret Classroom FeedbackMaram, Sai SiddarthaZaman, UliaEl-Nasr, Magy Seifhttps://hdl.handle.net/1721.1/1631252025-10-11T06:55:23Z2025-07-01T00:00:00ZListening with Language Models: Using LLMs to Collect and Interpret Classroom Feedback
Maram, Sai Siddartha; Zaman, Ulia; El-Nasr, Magy Seif
Our findings suggest that LLM-based feedback systems offer richer insights, greater contextual relevance, and higher engagement compared to standard survey tools. Instructors valued the system’s adaptability, specificity, and ability to support mid-course adjustments, while students appreciated the conversational format and opportunity for elaboration.
2025-07-01T00:00:00ZSupernote: Crowdsource the Best Ideas and Democratize Class NotesMandloi, NamanKopikar, Mokshhttps://hdl.handle.net/1721.1/1631242025-10-11T06:55:25Z2025-07-01T00:00:00ZSupernote: Crowdsource the Best Ideas and Democratize Class Notes
Mandloi, Naman; Kopikar, Moksh
Recognizing that students often struggle to capture all the information explained by the teacher due to various reasons, such as reading writing disabilities, language barriers, absences, and difficulty in listening and taking notes simultaneously, Supernote […] utilizes a Large Language Model (LLM) architecture to compile incomplete notes from multiple students, compare captured points, and synthesize comprehensive notes that include missing information from the teacher’s lesson.
2025-07-01T00:00:00ZCliniKiosk: An Innovative Technology to Expand Healthcare AccessAdhikari, Elahttps://hdl.handle.net/1721.1/1631232025-10-11T06:55:28Z2025-07-01T00:00:00ZCliniKiosk: An Innovative Technology to Expand Healthcare Access
Adhikari, Ela
This investigation proposes CliniKiosk (Figure 1), an Artificial intelligence (AI)-powered digital health kiosk designed to deliver real-time, evidence-based, multilingual, empathetic, and personalized health assessments that are adaptable to culturally diverse communities. In contrast to traditional health chatbots, it dynamically adapts to users by analyzing demographics, symptoms, and medical history to provide both empathetic and personalized health recommendations.
2025-07-01T00:00:00ZEmpowering Learners with a Low-Barrier Mobile Data Science ToolkitElhashemy, HanyaParks, RobertKim, David YJPatton, EvanAbelson, Haroldhttps://hdl.handle.net/1721.1/1630712026-03-23T03:23:43Z2024-10-01T00:00:00ZEmpowering Learners with a Low-Barrier Mobile Data Science Toolkit
Elhashemy, Hanya; Parks, Robert; Kim, David YJ; Patton, Evan; Abelson, Harold
This paper introduces a novel data science toolkit designed specifically for children, enabling them to create mobile apps integrated with data science capabilities. The toolkit showcases new features that simplify the data science process for young users. Additionally, the paper presents a collection of example apps created using the toolkit, highlighting the versatility and potential of this innovative platform. By empowering children to explore data science through app development, this toolkit opens exciting opportunities for hands-on learning and creative expression in the field of citizen science.
2024-10-01T00:00:00ZExploring Prompt Engineering for Generative AI-Based App GenerationShone, Jasmin L.Liu, RobinPatton, EvanKim, David YJhttps://hdl.handle.net/1721.1/1630692026-03-23T03:23:42Z2023-04-01T00:00:00ZExploring Prompt Engineering for Generative AI-Based App Generation
Shone, Jasmin L.; Liu, Robin; Patton, Evan; Kim, David YJ
We introduce a cutting-edge learning platform powered by large language models that enables students to effortlessly generate mobile applications for smartphones and tablets from natural language descriptions. We further demonstrate that these user-generated apps can be further optimized with minor adjustments to the generative model's input, or, its "prompt." To maximize the efficacy of the prompt in producing a desired application, we explore three different methods of modification: 1) altering the selection mechanism of example pairs, 2) varying the number of example pairs, and 3) changing the order of pairs within the prompt. The prompts are constructed from a collection of example pairs, which comprise a textual description of an example app and its corresponding code, in addition to a description of the desired app. We test the model's performance by evaluating it with 18 different mobile application task descriptions, ranging from basic to complex, and then leveraging BLEU score to compare the model's outputs to manually created apps. Our findings indicate that the method of determining example pair selection and varying the number of examples included can significantly influence the quality of the generated apps. However, reordering the placement of the example pairs within the prompt does not affect the outcome. Finally, we conclude with a discussion on the potential implications for computer science education. The platform we present in this paper aims to further the democratization of app creation through enabling users to create apps with ease, regardless of their technical background.
2023-04-01T00:00:00Z