FAS faculty members are invited to submit proposals for funding aimed at enhancing teaching and learning through the innovative use of Artificial Intelligence (AI). This initiative aims to encourage faculty members to explore and adopt AI-powered tools and methodologies that demonstrate the practical applications of AI in teaching and learning. It seeks to enhance student engagement and outcomes, foster innovation in course delivery, assessment, and curriculum design, and promote the ethical and responsible integration of AI within educational practices.
Eligibility: FAS faculty members. Faculty members who are on Leave without Pay for more than one semester during the grant's award period will not be eligible for funding. Each faculty member may submit only one proposal as a Principal Investigator for only one of the calls listed under 1. General and Interdisciplinary Opportunities and 2. Mamdouha El-Sayed Bobst FAS Deanship Fund.
Budget: Up to $5,500 per semester
Application materials: Apply online. Please upload in a single PDF file: 1. Proposal including abstract (no more than 200 words), description of the proposed activity and expected outcomes (no more than 1000 words), detailed budget and budget narrative (indicating the anticipated use of the requested funds and all funds sought or secured from internal and external sources), timeline; and 2. Letter of commitment from collaborating institutions or individuals, when available.
Application deadline: October 1; March 15
Contact: [email protected]
2026-27
- Self-directed Learning
Zeeshan Samad, Department of Economics
Abdallah Zalghout, Department of Economics
Artificial intelligence (AI) tools are rapidly changing how students access information and engage with course material. These systems can now provide explanations, examples, and analytical guidance instantly, potentially enabling students to explore unfamiliar topics independently. This project develops and implements a set of hands-on learning sessions designed to help students use AI tools productively when studying new material. The sessions will be integrated into selected recitation sections of ECON 211 (Elementary Microeconomic Theory), a large multi-section course enrolling approximately 600 students across 20 recitation sections. To evaluate the effectiveness of these sessions, recitation sections will be randomly assigned to either implement the AI-supported activities or follow the traditional format. Student performance on a task requiring independent learning of unfamiliar material will be compared across conditions, controlling for entering GPA. The goal is to develop effective teaching practices that incorporate AI-supported self-learning and to generate evidence on how AI tools influence independent learning in higher education.