AIx focuses on ways in which Artificial Intelligence can make learning accessible and engaging for all individuals. These include:
- AI for Assistive Technology. AI can help all individuals through technologies such as captioning, voice recognition, and video description. One specific focus is on AI's potential to increase the accuracy and efficiency of Speech-to-Text and Text-to-Speech to help caption videos. Led by Dr. Li Liu from Computer Science.
- AI in Teaching and Learning. AI can assist learning in various ways. This semester we are focusing on how virtual tutors can help students learn material by clarifying the questions they are asking, providing helpful answers, and removing the intimidation factor. Led by Dr. Mariano Loza-Coll from Biology.
- AI for Personalized Classrooms. Virtual assistants are now commonplace in the home for controlling appliances and providing information. How might they be used in the classroom? This semester we are exploring faculty use cases for how Alexa can be used creatively in the teaching spaces and beyond. Led by Dr. Deone Zell, AVP for Academic Technology.
- AI in Research. AI can further research by helping to identify patterns in all disciplines, from art to engineering. This semester, interested faculty from across campus are meeting regularly to identify opportunities for funding and collaboration. Meeting dates coming soon. Led by Dr. Crist Khachikian, AVP for Research & Graduate Studies.
- AI-Jam II. This cross-disciplinary competition, held in Spring 2019, will challenge students to create applications of AI that can make learning accessible and engaging for all individuals. Students will be invited to work in teams and draw talent from all interested majors to compete for prizes and recognition.
In addition to support from Information Technology, the initiatives were co-led by two CSUN faculty members: Dr. Mariano Loza-Coll from Biology, and Dr. Li Liu from Computer Science, in collaboration with CSUN's Disability Resources and Educational Services (DRES), National Center on Deafness (NCOD), and the Deaf Studies Department in the Michael D. Eisner College of Education at CSUN.
Photo Credit: Jean-Francois Podevin via The New York Times
"Countless dollars and entire scientific careers have been dedicated to predicting where and when the next big earthquake will strike. But unlike weather forecasting, which has significantly improved with the use of better satellites and more powerful mathematical models, earthquake prediction has been marred by repeated failure."