Biography
Salih Mansur is a Ph.D. student in Learning Sciences at Adelphi University. His interdisciplinary work integrates artificial intelligence in education, instructional technology, educational psychology, digital literacy, multimedia learning, and human-centered design. He holds graduate degrees in AI Systems, Instructional Technology, and Communication Arts. He has contributed to academic conferences, research presentations, professional organizations, and educational technology initiatives. His work examines how AI-supported learning environments can strengthen learner agency, critical thinking, reflection, meaning-making, and responsible decision-making without weakening human judgment or relationships. He is developing the All-in-One (Ai1) Approach, a framework designed to make AI-generated information more structured, accessible, practical, and educationally meaningful. His broader research interests include cognitive load, self-regulated learning, adult and lifelong education, culturally responsive learning design, and the ethical integration of emerging technologies. He is also actively involved in professional service, scholarly review, educational innovation, and interdisciplinary work connecting technology, psychology, communication, and learning.
Conference contributions 1
DESIGNING HUMAN-CENTERED AI-SUPPORTED LEARNING ENVIRONMENTS:
The all-in-one (ai1) approach to agency, critical thinking, and meaning-making
Artificial intelligence can expand access to information, yet many AI-generated responses remain fragmented, overly detailed, or insufficiently adapted to learners' needs. This conceptual paper introduces the All-in-One (Ai1) Approach, a human-centered framework for designing AI-supported learning environments that prioritize clarity, meaning-making, learner agency, and critical thinking. Rather than presenting information as an unstructured answer, the Ai1 Approach organizes content through a layered structure that begins with the main idea, followed by concise explanation, relevant connections, practical application, and reliable sources. The framework is informed by principles from learning sciences, cognitive load theory, multimedia learning, and self-regulated learning. It is intended for use across formal and informal educational settings, including higher education, adult learning, digital literacy, and lifelong learning. The paper argues that adaptive AI should not merely deliver information efficiently, but should help learners interpret, question, connect, and apply knowledge while maintaining meaningful human control. It also considers ethical concerns related to overreliance, bias, transparency, and the possible weakening of independent judgment. The Ai1 Approach offers a practical direction for educators, designers, and researchers seeking to develop AI-supported learning systems that are accessible, psychologically informed, and responsive to diverse learners. Future research should test how this structured approach influences comprehension, cognitive load, engagement, agency, and critical thinking across different populations and learning contexts.