AI Initiatives

Learning with AI Spark Awards

"Ignite small sparks of innovation"

($500.00 award)

Who should apply? Individual instructors of record looking to explore AI for the first time in their own course and/or with their students. 

*Please note that previous Tier 1 *winners* are not eligible to apply. Previous applicants who were not selected are welcome to submit revised concepts.

• Purpose: This tier supports small, quick-win projects that are ready to implement immediately in Fall 2025. These awards are designed to encourage exploration and experimentation with AI in small but impactful ways. These projects should have clear, achievable outcomes for enhancing classroom experiences with AI that benefit both instructor and student understanding of AI. These projects are not designed to support individual student paid subscriptions for various AI tools. NJIT provides access to Google Gemini and Notebook LM. Instructors are advised to structure their explorations leveraging these tools for Tier 1 awards.

• Examples: Student use of AI to achieve *existing* course learning outcomes while learning about AI, evaluating different AI capabilities with students, exploring use-cases. 

• Evaluation Criteria: Proposals will be evaluated by the CEIE Advisory board, and selections will be based on feasibility for the upcoming semester rollout, impact on student engagement or learning, and minimal required resources. Projects should demonstrate measurable results with low setup time.

Timeline for Round 2:

Applications Due: June 30, 2025

Decisions Announced: August 11, 2025

Implementation: Fall 2025 semester

Surveys: Mid November, 2025

Tier 2: AI Pilot Seed Grants ($2,000 - $10,000) | Spring 2026 Implementation | Round 2 Open Now!

"Plant the seeds for scalable AI innovation in the classroom"

Categories:

1. Instructor/Course Coordinator: For faculty looking to pilot innovative AI-driven tools or methods within a specific course or set of courses they oversee. Coordinators must provide a letter from the Department Chairperson indicating approval and support to implement the initiative across sections.

2. Department/Program: For departments or programs aiming to implement AI across multiple courses, programs, or centers, potentially impacting a broader student cohort. This category does not need to be tied to specific courses or for-credit experiences; anything that impacts students and their educational experience is applicable. If submitting on behalf of a department or program, a letter indicating Department Chairperson or Program Director approval and support to implement the initiative is required.
    

*Please note that previous Tier 2 *winners* are not eligible to apply again at this time. Previous applicants who were not selected are welcome to submit revised concepts.

• Purpose: This tier supports projects with the potential to grow. The Pilot Seed Grants require thoughtful planning and provide a foundation for more expansive AI initiatives, allowing faculty and departments to pilot innovative ideas that could lead to major transformations with a larger reach and more extensive implementation. 

• Examples: Examples include: AI to personalize learning pathways, support collaborative student projects, develop assessment tools with adaptive feedback, pilot a peer feedback system powered by AI, or create content delivery models tailored to student learning styles.

• Evaluation Criteria: Proposals will be reviewed by the CEIE Advisory Board, and selections will be made based on clear implementation steps and timeline in preparation for the Spring 2026 semester, scalability potential, and a detailed budget plan. This tier encourages interdisciplinary approaches and projects that could be refined and scaled across courses or departments.

All winning applicants will be asked to distribute a CEIE-designed survey to any students/participants at the end of the Spring 2026 semester.

Timeline for Round 2:

Applications Due: September 1, 2025

Decisions Announced: October 6, 2025

Implementation: Spring 2026 semester

Surveys: End of April, 2026

CEIE CFP: Tier 3 AI Curriculum and Instruction Innovation Grants

The wait is over! It’s time to get started on Tier 3 Innovations

• Purpose: This top-tier grant is for groundbreaking, forward-thinking projects that reimagine teaching and learning through AI. It is intended for complex, high-impact proposals that may need a longer development timeline or larger resource commitment.  Before applying, please consider whether your proposal fits better as a Tier 1 or Tier 2 project (Table 1), and review examples of competitive Tier 3 grant ideas at the end of this call. 

• Please note that Tier 1 and 2 will likely open for a second round soon, and may provide excellent opportunities to pilot concepts for stronger Tier 3 proposals.
   

Table 1. AI Curriculum and Instruction Innovation Grants - Comparison Table

• Submission process --SUBMISSIONS ARE CURRENTLY CLOSED.

  1. Project description: project title, goals, alignment with existing course/student learning outcomes, as applicable, or inclusion of measurable objectives with justification for student learning impact, and methodology. Additional consideration will be given to projects emphasizing cross-disciplinary impact.

  2. Budget and justification for resources: detailed financial plan outlining all anticipated expenses, including personnel costs, materials, and overhead. Additional consideration will be given to proposals that use innovative resources and set a new standard of teaching and learning practices.

  3. Plan for implementation: Timeline, ethical considerations, and letters of support from Chair/Directors, IST, etc. Additional consideration will be given to the sustainability/project’s long-term viability. 

  4. Plan for evaluation: A plan for measuring the student perceptions of the project's effectiveness and a plan for measuring the impact on student learning, including indicators, data collection methods and instruments, and reporting mechanisms.

  5. Supporting documents, as necessary. 
      

Competitive Tier 3 AI grant examples: 
 

• Overlay physical classroom spaces with an augmented AI-powered digital “layer” accessed via AR glasses or mobile devices — transforming lecture halls into intelligent, responsive environments.

• Transformative AI Teaching Assistants that move beyond basic information retrieval chatbots to become an intelligent, empathetic, and proactive teaching assistant that can enhance student experience through dynamic ever-changing learning environments tailored to student need. The teaching assistant anticipates needs, understands context deeply, personalizes interactions, and actively helps students achieve their relevant goals.

• Use advanced AI translation and cultural mediation tools to create real-time cross-campus, cross-border learning modules — allowing NJIT students to collaborate live with international peers.

• Every student is paired with an AI "cognitive twin" — an evolving model of how they learn, think, struggle, and succeed built from behavioral, performance and preference data, which are used to simulate future performance and “what if” learning pathways. This creates a feedback loop where students learn how they learn.

• Create a gamified AI learning platform where every student’s academic journey is framed as an evolving epic myth — tailored to their discipline, values, and goals.

• A modular learning system where the curriculum builds itself based on student interest, AI prediction of industry trends, and social relevance.
   

If you have any questions, please email ceie@njit.edu. We look forward to receiving your innovative ideas!

Tier 1 Awards, Round 1 (Spring 2025)

1. Dr. Yanxiao Feng
   Course- CET423- Construction Safety, 
   Project Title- “Enhancing Construction Safety Education with Beginner-Friendly AI and Sensing Technologies.”

2. Dr. Andrew Pole
   Course- MATH447- Applied Time Series Analysis,
   Project Title- “Understanding and Communicating Math: Help from LLMs.”

3. Dr. Jaskirat Sodhi, Dr. Samuel Lieber and Mr. Joseph Beshay
   Course- MET450-Mechanical Design Capstone Project,
   Project Title- “Integrating AI into a senior design project course.” 

4. Dr. Jaskirat Sodhi, Dr. Samuel Lieber and Mr. Majd Awad
   Course- MET415- Automatic Control Systems
   Project Title- “Integrating AI into an automation course.”

5. Dr. Jaskirat Sodhi, Dr. Samuel Lieber, Dr. Sahidur Rahman, Mr. Daniel Orlos and Mr. Mark Lanfrank
   Course- MET103- Engineering Graphics and Introduction to CAD,
   Project Title- “Integrating AI into a first year engineering design course.” 

6. Dr. Kathleen Naasz
   Course- ENTR210-Intro to Entrepreneurship,
   Project Title- “Accelerate your Startup Plan with AI.”

7. Dr. Lou Kondic
   Course- MATH451H- Methods of Applied Mathematics II (Capstone II)
   Project Title- From structure to performance of porous media using image processing, topological data analysis and machine learning

8. Dr. Aneliia Chatterjee
   Course- COM313-Technical Writing,
   Project Title- “AI Writing Assist: Enhancing Technical Communication with Notebook LM, Grammarly, and Integrated Research Tools.”

9. Melissa Valoura
   Course - CIM215- Concrete Applications II,
   Project Title- “AI-Powered Concrete: Mix Design Through Intelligent Technology.”

10. Dr. Chang Yaramothu
    Course- BMET320- Applied Biomedical Data Acquisition,
    Project Title- “LLM Assisted Creation of Imitation Human Phantoms.”

11. Dr. Joseph Micale
    Course- ACCT335- Managerial Accounting II,
    Project Title- “Implementing AI in the Accounting Curriculum: Evidence from the Cost-Volume-Profit Relationship.”

12. Dr. Wenbo Cai
    Course- IE459-Supply Chain and Production Planning,
    Project Title- “AI-Drive Excel Models for Robust Supply Chain Management Solutions.”

TIER 1 Awards, Round 2 (Fall 2025)

1. Mohamed Mahgoub
   Course: CIM205
   Project Title: "Integrating the Neural Network Model (NNM) for AI-Driven Design Learning"

2. Johanna M Deane
   Course: ENGL101
   Project Title: “Arguing Against the Machine: Using Adversarial AI as a Training Partner in Written Composition”

3. Jake Slovis
   Course: ENGL102 Honors
   Project Title: "AI as a Tool to Examine the Lexicon and Social Dynamics of the Writing Classroom "

4. Drew Ciccolo
   Course: ENGL101 and 101 Honors
   Project Title: “Inspiring Students to Think Critically about Generative AI and to Use it Ethically and with Discretion in First-Year Writing Courses and Beyond”

5. Jixing Li
   Course: FIN218
   Project Title: “Learning with AI, not from AI: Enhancing Student Reasoning through Reflective Critique”

TIER 2 Grants, Round 1 (Fall 2025)

1. Dr. Elisa Kallioniemi 
   Department: Biomedical Engineering
   Description: This proposal will use AI to personalize learning pathways in digital signal processing by using MATLAB AI Chat Playground as an additional teaching assistant in the course. 

2. Richard Thompson, Martina Decker and Raafi Rivero 
   Department: Hillier College of Architecture and Design, Art + Design
   Description: This proposal will integrate AI-powered text-to-video platforms (Sora, Adobe Creative Cloud) into the Digital Design curriculum by piloting the initiative with students in a summer exploration and a pilot elective course. 

3. Dr. Samuel Lieber
   Department: School of Applied Engineering and Technology
   Description: The project will introduce Prompt Engineering into a multidisciplinary engineering curriculum. The proposal provides students with formal training on use of AI in technical communication (e.g., engineering report, proposals) and will make critical strides in developing a prompt engineering curriculum assessment tool.
    

TIER 2 Grants, Round 2 (Spring 2025)

1.  Courtney Self, Richard Thompson
   Department: Theater, Digital Design
   Course/Program: Theatre Arts & Technology and Digital Design
   Description: “Creating New Theatrical Experiences in the Age of AI Through Digital Embodied Storytelling,” brings together NJIT’s Theatre Arts and Digital Design programs to explore how AI-driven, markerless motion capture (MoCap) technologies can transform live performance and digital storytelling. Using tools like Autodesk Flow Studio and Movin Tracin, the project enables students to animate digital characters in real time through authentic human movement, blending performance, design, and technology. The work will culminate in the Spring 2026 theatre production of Curtains, where a live MoCap performance will be integrated into the show. Beyond the production, the initiative will inform new interdisciplinary curricula, giving students hands-on experience with emerging AI tools, enhancing their technical and creative skills, and positioning NJIT at the forefront of innovative, AI-enhanced performing arts education.
    
2. . Yanxiao Feng, Melissa Valoura; John Wiggins
   Department: The School of Applied Engineering and Technology (SAET)
   Course/Program: The Built Environment Division (SBED)
   Description: “AI Integration Across the Built Environment Curriculum,” aims to embed applied artificial intelligence tools directly into NJIT’s Construction Engineering Technology (CET) and related programs to modernize Building Information Modeling (BIM)–based instruction. Using platforms such as Autodesk Generative Design, Dynamo, OpenCV, and ChatGPT, the project introduces AI for generative design, computer vision–based safety analysis, and adaptive feedback in both in-person and online construction courses. Students will use these tools to optimize layouts, identify site hazards, and automate documentation—mirroring emerging industry workflows—while instructors use AI to scale feedback and personalize instruction. Implemented across multiple construction-focused courses in Spring 2026, this initiative will strengthen students’ technical fluency, problem-solving, and communication skills while positioning NJIT’s Built Environment Division as a leader in AI-enhanced design, construction, and safety education
   .
3. Mohamed Maghoub
   Department: School of applied Engineering and Technology (SAET)
   Course/Program: Concrete Industry Management (CIM)
   Description: “AI-Assisted Concrete Mix Design for Engineering Education,” expands NJIT’s Concrete Industry Management (CIM) curriculum by developing a custom neural network that allows students to use AI to design concrete mixes based on target strength and performance criteria. Rather than predicting outcomes from existing data, the model will reverse the process—students input desired strength values, and the AI suggests optimal material proportions, which they will then prepare and test in the lab. This hands-on cycle of AI prediction, physical testing, and data feedback helps students learn how to evaluate and refine machine-generated outputs, strengthening both their technical judgment and data literacy. The project bridges computation and experimentation, positioning AI as a design partner in materials science education and paving the way for future applications across other construction materials such as steel, wood, and asphalt.