Research-based development in authentic educational settings.
AIONize emphasizes iterative development, usability, feasibility, classroom implementation, and evidence-informed product refinement.
Research-based development
AIONize’s work is grounded in authentic course deployment, instructor feedback, student feedback, and iterative educational technology development.
AICC has been used and studied in postsecondary computing and technology courses, including programming, database systems, computer organization, automata theory, cybersecurity, and malware analysis.
Evidence focus
Prior implementation work has examined feasibility, usability, perceived learning support, instructor acceptability, adoption patterns, and responsible implementation.
Early implementation evidence
AICC implementation work has generated evidence relevant to product refinement and future evaluation.
Student adoption
Studies examine whether and how students use course-grounded AI support in authentic courses.
Perceived value
Student and instructor feedback inform usability, feasibility, and perceived learning support.
Instructional insight
Instructor-facing reports and usage patterns help identify frequent questions and possible learning needs.
Current development focus
AIONize is currently focused on developing and evaluating LearnLoop as a new component that adds active learning verification to AICC.