Ongoing GenAI Research Projects and Contributions in Learning Sciences
Join Me at MassCUE for a New Era of Computing in Education.
Discover privacy-first, on-device AI applications in education at MassCUE Fall 2024!
Join me on Thursday, October 17, at Gillette Stadium in Foxborough, MA, in the Educator Poster Session.
For a deeper look into these innovative solutions, view the full poster:
Are you a K-12 Math Teacher or Tutor? We made a chatbot for you!
Discover Estella Explainer Math Bot 2 (EEMB 2), a GPT-4 powered chatbot that simplifies math word problems to enhance student understanding and engagement.
Chat with the Estella Explainer Math Bot 2 here. Or click anywhere on the image above.
Our free chatbot is designed to assist elementary math teachers and tutors worldwide teaching math topics in English. Powered by ChatGPT, our AI tool offers personalized readability support for math word problems, provides valuable math resources, and introduces interactive problem-solving techniques. Our goal is to enhance both teachers' and students' understanding and engagement in mathematics across formal and informal learning environments, as it helps educators (pre-service and in-service) improve their programs and practices.
In our internal testing, the EEMB 2 chatbot has consistently outperformed our expectations. While we are continuously making improvements to the chatbot, you may sometimes experience dissatisfaction with the performance of the bot. I encourage EEMB 2 users to keep refining your prompts and try again. If you have any feedback, I would love to hear: sgattupalli at umass dot edu.
A free ChatGPT account is required. While we do not collect any user inputs, ChatGPT does, and uses it for their model training purposes. Users must be at least 13 years old to use ChatGPT.
Technicals of EEMB 2:
Aligned with Usable Math's teaching philosophy.
Read more on NCTM and Digital Experiences in Mathematics Education.
10-shot learning for fine-tuning, with prior Massachusetts Comprehensive Assessment System (MCAS) test items with human-crafted hints.
Image source: Unsplash.
Employs Flesch–Kincaid readability metric for math explanations. See decision tree on our blog.
Image source: Wiki.
Prompting instructions based on Universal Design for Learning (UDL) principles.
Image source: Unsplash.
I developed EEMB 2 in collaboration with math education faculty:
See how we are use generative AI technologies in elementary math teaching and learning contexts.
Screenshot showing Usable Math's AI-Enhanced Learning page. Click on the image, or click here to visit.
Encouraging Ethical & Responsible AI Use in College Writing
In my College Writing course (ENGLWRIT 112) at UMass Amherst, I aim to equip my students with the skills to use generative AI tools such as ChatGPT and Claude responsibly and ethically. By emphasizing prompt literacy and ethical AI usage, I guide my students to critically engage with these tools, ensuring that AI enhances, rather than replaces, their creative and analytical efforts. This approach fosters a balance between AI’s capabilities and the essential human skills of critical thinking and writing.
Read current AI usage policy (FA 2024). Previous iteration available on Lance Eaton's Syllabi Policies for AI Generative Tools. More on my blog article.
Prompt Literacy bridges Human Creativity and AI in Modern Education
Read my research paper titled "Prompt Literacy: A Pivotal Educational Skill in the Age of AI," where we explore the critical role of effective prompting in achieving desired outcomes from foundation models. We emphasize how mastering prompt literacy is essential for both educators and learners to harness the full potential of AI technologies in education.
This work is a collaborative effort with esteemed colleagues Dr. Robert Maloy and Dr. Sharon Edwards from the College of Education, University of Massachusetts Amherst.
Cite our work (APA):
Gattupalli, S., Maloy, R. W., & Edwards, S. A. (2023). Prompt literacy: A pivotal educational skill in the age of AI. https://doi.org/10.7275/3498-wx48
Investigating Pre-Service Teachers' Perceptions of LLM-generated Math Hints in Enhancing Online Mathematics Education. Honored with presentation in AIED 2023.
In this study, we explore pre-service teachers' perceptions of large language model (LLM)-generated hints in the context of online mathematics education. This research focuses on the potential of LLMs, such as GPT4, to provide walkthroughs and guidance in math problem-solving. While human-generated content, especially visual aids, remains preferred, LLM-generated hints show promise in enhancing the teaching process. Presented at AIED 2023, this paper highlights both the opportunities and challenges of integrating AI into math education and suggests directions for future development.
Cite our work (APA):
Gattupalli, S. S., Lee, W., Allessio, D., Crabtree, D., Arroyo, I., Woolf, B. P., & Woolf, B. (2023). Exploring Pre-Service Teachers' Perceptions of Large Language Models-Generated Hints in Online Mathematics Learning. In LLM@ AIED (pp. 151-162). https://ceur-ws.org/Vol-3487/paper10.pdf.
In collaboration with:
Will Lee, Danielle Allessio, Danielle Crabtree, Ivon Arroyo, & Beverly Woolf––from the University of Massachusetts Amherst.
Banner Image Reference:
Cukurova, M. (2024). The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13514.