In its sixth year, DELTA provided funding for five projects selected from a competitive field of 15 proposals. Each of the funded projects will deploy innovative uses of digital technology to enhance the university’s teaching and learning enterprise for a wide variety of populations, including Johns Hopkins graduates, undergraduates, faculty, students in the Center for Talent Youth programs, and the general public.
Principal Investigators: Sean Tackett, Belinda Chen, and Shiv Gaglani, School of Medicine; Nehal Khamis, School of Education; Caroline Egan, Krieger School of Arts and Sciences
Our multidisciplinary, multidivisional team will test and evaluate a variety of generative artificial intelligence (GenAI) technologies across five formats of faculty development programs that teach the same curriculum development principles to health professions educators. These formats include ½ day sessions (twice a year), 2-day programs (four times a year), a curriculum revision program (orientation followed by monthly mentored sessions), and a 10-month longitudinal program (with weekly half-day sessions) taught in the School of Medicine, and a semester-long course taught in the School of Education’s Master of Education in the Health Professions (MEHP) program. By iteratively testing, evaluating, and improving the application of GenAI in these programs, we will identify an array of opportunities to apply GenAI in faculty development. Giving faculty participants firsthand experience in using and critiquing GenAI will also make them better prepared to apply it judiciously in their future educational practices. By training the trainers, we will optimally leverage the resources from the grant to disseminate innovations into practice. Finally, by improving quality and decreasing costs of faculty development, we can enhance value, access, and equity in these educator programs and provide a model that could be used across Johns Hopkins University and more broadly.
Principal Investigators: Kimberley Chandler, Center for Talented Youth; Daniel Khashabi and Benjamin Van Durme, Whiting School of Engineering; Jennifer Morrison, School of Education
Artificial intelligence (AI), specifically large language models (LLM), have burst into the educational field with dramatic effect in the last six months. How the field of education responds to this development is a work in progress, and this project is designed to introduce some structure to how to begin to respond to this innovation. This project is designed to proactively prepare both students and teachers to maximize the potential of AI in the learning process. To accomplish this, we will modify instructional materials and courses to instruct K12 students on the ethical use of AI tools such as ChatGPT, Bing’s Chatbot, Google’s Bard, etc. in formal coursework assignments. Materials and courses will be modified to concentrate on assignments and assessments that focus on higher levels of processes such as analyzing, evaluating, and creating. A standard rubric of achievement will be used to measure student performance on course objectives to compare students in AI supported courses with a control group of students in a traditionally instructed course. For test students and teachers, an AI LLM will be selected with basic guardrails to help assist the student in pondering their own responses and locating reliable online sources to support their work, instead of merely providing students with answers to questions. Teachers will be trained in how to instruct students to use AI tools as supportive to learning. The control group students would be assigned the same academic lessons but not provided with the AI tool or training in the use of AI tools. A comparison of students’ performance could then be conducted to determine the efficacy of AI LLM support in K12 learning. This effort is a joint proposal from the Center for Talented Youth, a team from the Department of Computer Science, Whiting School of Engineering, and the School of Education’s Center for Research and Reform in Education. A successful pilot project would have rapid impact through presentations at professional conferences (e.g., International Society for Technology in Education (ISTE), National Association for Gifted Children (NAGC), and the American Educational Research Association (AERA)) and serve as the basis for applications for federal funds such as the NSF’s, Rapidly Accelerating Research on Artificial Intelligence in K-12 Education in Formal and Informal Settings (link).
Principal Investigators: Christina Harnett and James Diamond, School of Education; Tracy Friedlander, School of Medicine
How can online courses be designed, and pedagogies be implemented, such that they support academic learning objectives and promote digital well-being? As online teaching and learning continue to proliferate at Johns Hopkins and other institutions of higher education around the world, faculty and instructional designers must become familiar with and enact course design frameworks and pedagogies that support digital well-being among their students. We propose to create, teach, and disseminate a professional development (PD) program to help JHU educators foster their students’ digital well-being. The program will introduce educators to two related frameworks, and accompanying best practices, they can use to align teaching practices, class norms, and digital learning activities to the goals of digital well-being. Instructors and course designers who participate in the PD will be prepared to integrate the digital well-being frameworks into their online course development processes. The PD program model is flexible and can be used in multiple divisions of the university, and in other settings.
Principal Investigators: Francis Deng and Jenny X. Chen, School of Medicine; Chien-Ming Huang and Paul Yi, Whiting School of Engineering
As medical imaging grows in complexity and volume, effective and efficient training methods in radiology are critical to meet these increasing demands. Deliberate practice has been recognized as a useful method for cultivating expertise in various fields, including medicine, as it offers learners a purposeful strategy for skill development that combines focused, goal-oriented efforts and continuous feedback in an intentionally repetitive manner. To facilitate deliberate practice in the training of medical professionals to interpret radiologic images, we propose to develop a high-fidelity simulation platform that integrates artificial intelligence (AI)-generated feedback for learners. The platform will emulate the diagnostic Picture Archiving and Communication System (PACS) used in clinical settings, incorporate a comprehensive collection of cases, and provide AI-augmented feedback. The system will also adapt to learners’ prior progress, creating individualized learning experiences for precision medical education. We will pilot this technology by designing and evaluating a module to teach trainees to identify intracranial hemorrhages on head CT scans in a randomized, controlled educational intervention study. In the future, this innovative platform technology has the potential to be customized for teaching a wide variety of imaging diagnoses, transforming the way medical professionals are trained in radiology and other visual diagnosis-based specialties.
Principal Investigators: Olysha Magruder, Margo Williams, Denille Williams, and Melissa Rizzuto, Whiting School of Engineering; Rickey Chapman, Applied Physics Laboratory
The proposed Science, Technology, Engineering, and Math for Active Learning Laboratories (STEM for ALL) program offers three components to prepare instructors to incorporate research-based active learning strategies in in-person, blended, or online STEM courses. These components include: 1) a self-paced Massive, Open, Online Course (MOOC) delivered through Coursera, 2) a catalog of interactive Active Learning Laboratories (ALLs), and 3) an ongoing, collaborative Community of Practice (CoP) based on the Scholarship of Teaching and Learning (SoTL) in which participants share their experiences, results, and examples from implementing active learning in their work. The goal of the innovation is to 1) provide a self-paced foundations course about active learning in science, technology, engineering, and math (STEM) disciplines, 2) support higher education faculty in implementing active learning strategies to improve student learning outcomes, and 3) develop a community of practice and scholarship of teaching and learning around active learning in STEM disciplines at JHU and beyond. The target audience includes Johns Hopkins faculty, graduate students, post-doctoral fellows, and staff and additional audiences include instructors and instructional support staff outside of Johns Hopkins.
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