Digital Integration Teaching Initiative

Last updated: January 8, 2025

The work of the Digital Integration Teaching Initiative is complementary to other initiatives to foster AI literacy across Northeastern University. The university currently supports the “deep adoption of AI technologies”. Faculty may differ on the extent to which they prefer to integrate AI into their courses. This page summarizes resources that may be useful in choosing whether and how to integrate AI into a course.

The Northeastern Library, Center for Advancing Teaching and Learning Through Research (CATLR), Information Technology Services (ITS), and DITI offer resources that may be helpful in determining the appropriate level of AI integration and in course development. Even for courses not integrating AI, developing an understanding of AI may help facilitate conversations with students and aid in the creation of effective course policies. 

• Building foundational knowledge: the Northeastern Library offers introductory asynchronous tutorials on AI Basics, Prompt Engineering in AI, Generative AI, Evaluating AI Results, and Citing and AI. 
• Northeastern AI policies: faculty can visit the CATLR page on Policies, Guidance, & Resources. 
• Uses of AI in education: CATLR maintains an AI gallery showcasing various uses as well as a self-paced course and other tutorials.  ITS Academic Technologies experts also offer consultations on AI for teaching.
• Northeastern University AI tools: the Library provides information on university investment in AI-powered information resources and Northeastern Information Technology Services (ITS) maintains a list of university-supported AI services. 
• Specific applications of AI technologies: DITI offers accessible asynchronous short-form teaching materials focusing on the application of particular AI tools. At present, we have published teaching materials on AI for literature reviews (including demonstrations of how to use ChatGPT, Claude, Elicit, and Litmaps) and on Scite. These materials take a critical approach to using these tools and discuss their weaknesses, as well as how to counter those weaknesses. We plan to continue expanding our library of teaching materials on AI tools.

The Library and DITI also offer in-class support. These in-class visits can help build AI literacy for students around both the strengths and weaknesses of AI, as well as appropriate and inappropriate uses. 

• For in-class support in teaching introductory media literacy surrounding AI and large language models (LLMs), faculty can contact Lawrence Evalyn, the library Text Mining Specialist. 
• The Library also offers a variety of workshops on AI. 
• The DITI complements these resources and initiatives by offering in-class customized modules and workshops that help students further apply AI literacy skills to class-specific learning objectives. Each semester, we will send out a call for partnerships for faculty interested in working with us the following semester. For more information and specific timeframes for when the call for partnerships is open please refer to our Faculty Partnership Guidelines. In Spring 2026, we will also continue growing our asynchronous resources to include teaching materials on AI.   

Please feel free to contact us at [email protected] if you are aware of any Northeastern University resources for teaching and learning with AI not included on this page.