Author ORCID Identifier
Dr. James Lipuma, es el director del Collaborative Collaborative for Leadership, Education, and Assessment Research (CLEAR) del New Jersey Institute of Technology. NJ. USA. lipuma@njit.edu (primer autor). ORCID: https://orcid.org/0000-0002-9778-3843
Dr. Cristo León, es el director de investigación de la facultad de artes y ciencias liberales Jordan Hu del New Jersey Institute of Technology. NJ. USA. leonc@njit.edu (autor corresponsal). ORCID: https://orcid.org/0000-0002-0930-0179
Dra. Yi Meng, es la directora asociada del Survey Research en la Office of Institutional Effectiveness del New Jersey Institute of Technology. NJ. USA. Yi.meng@njit.edu. ORCID: https://orcid.org/0009-0004-5246-228X
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Document Type
Article
ISBN
979-8-89020-139-3
Publication/Submission Date
8-1-2025
Keywords
AI-native learners, assessment redesign, cognitive integrity, instructional design, metacognition, transdisciplinary communication, transparency, Comunicación transdisciplinaria, Diseño instruccional, Estudiantes nativos de la inteligencia artificial, Integridad cognitiva, Metacognición, Rediseño de la evaluación, Transparencia
Disciplines
Arts and Humanities | Education | Social and Behavioral Sciences
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Lipuma, James Ph.D.; Leon, Cristo Ph.D.; and Meng, Yi Ph.D., "From Digital Natives to AI-Natives: Rethinking Assessment and Instructional Design in the Age of Autonomous AI Tools" (2025). STEM for Success Resources. 121.
https://digitalcommons.njit.edu/stemresources/121
Comments
Data Availability Statement
I confirm I have included a data availability statement in my main manuscript file. All data supporting the findings of this study are openly available on the Open Science Framework (OSF) at the following DOI: https://osf.io/m7k39. The data are shared under a Creative Commons license and include the Institutional Review Board (IRB) protocol, interview protocol, coding framework, and data visualization model components.