AMI has expertise in speech recognition, educational assessment, product development, machine learning, automated scoring, and psychometrics. Below you will find a collection of key presentations and research articles authored by AMI team members.
Funded by a grant from the Small Business Innovation Research (SBIR) Insosite of Education Sciences of the U.S. Department of Education, AMI demonstrated the technical feasability of using artificial intelligence to develop an automated diagnostic system for oral reading fluency performance. Skillcheck proves teachers diagnostic information to help address the skills students need to improve foundational reading skills and achieve fluent oral reading.
Virtual Interview Practice Partner (VIPP) Product Concept
Built and validated with National Science Foundation funding, the Virtual Interview Practice Partner (VIPP) mobile application is an automated, self-assessed interview practice tool that focuses on communication skills and situational judgement tasks. Students can prepare for competitive job interviews with VIPP by practicing how to demonstrate their professional communication, collaboration, and problem-solving skills in ways employers value.
*Learn more about AMI’s product concept and roadmap for the (VIPP) here
Papers and Presentations
Bernstein, J. (January 2022). Machine learning methods behind AI applications in Education. Annual IES Principal Investigators Meeting: Advancing Education and Inclusion in the Education Sciences.
Bernstein, J., Suzuki, M., Cheng, J., Yang C., Ciancio, D., Brenner, D. (2020). Automated Estimation of Foundational Reading Skills from Recorded Passage Read-Alouds. California Educational Research Association (CERA) 99th Annual Conference, Anaheim, CA.
Bernstein, J., Cheng, J., Balogh, J., & Downey, R. (2020). Artificial intelligence for scoring oral reading fluency. In H. Jiao & R. Lissitz (Eds.), Applications of artificial intelligence to assessment. Charlotte, NC: Information Age Publisher.
Ramachandran, L., Cheng, J., & Foltz, P. (2015). Identifying patterns for short answer scoring using graph-based lexico-semantic text matching. Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Education Applications, 97–106.
Cheng, J., D'Antilio, Y. Z., Chen, X., & Bernstein, J. (2014). Automatic assessment of the speech of young English learners. Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications, 12–21.
Balogh, J., Bernstein, J., Cheng, J., Van Moere, A., Townshend, B., & Suzuki, M (2012). Validation of automated scoring of oral reading. Educational & Psychological Measurement, 72(3), 435-452.
Bernstein, J. & Cheng, J. (2008). Logic and validation of a fully automatic spoken English test. In V.M. Holland & F.P. Fisher (Eds.), Speech technologies for language learning. Lisse, NL: Swets & Zeitlinger.
Balogh, J. & Bernstein, J. (2007). Workable models of standard performance in English and Spanish. In Y. Matsumoto, D.Y. Oshima, O.R. Robinson, & P. Sells (Eds.), Diversity in language: Perspective and implications (pp. 20-41). Stanford, CA: Center for the Study of Language and Information Publications.
Rosenfeld, E., Massaro, D., & Bernstein, J. (2003). Automatic analysis of vocal manifestations of apparent mood or affect. Proceedings of the Third International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, 5-8.