Voicu, R. C., & Tanveer, M. H., & Tekes, A. (2025, March), AI-Human Transference Learning and Assessment: Optimizing Knowledge Transfer and Understanding through AI-Generated Contextualization  Paper presented at 2025 ASEE Southeast Conference , Mississippi State University, Mississippi. 10.18260/1-2--54140

\bibitem{asee_peer_54140}
  Voicu, R. C., \& Tanveer, M. H., \& Tekes, A. (2025, March), \emph{AI-Human Transference Learning and Assessment: Optimizing Knowledge Transfer and Understanding through AI-Generated Contextualization}
  Paper presented at 2025 ASEE Southeast Conference , Mississippi State University, Mississippi.
  10.18260/1-2--54140

Razvan Cristian Voicu, Muhammad Hassan Tanveer, and Ayse Tekes.  "AI-Human Transference Learning and Assessment: Optimizing Knowledge Transfer and Understanding through AI-Generated Contextualization".  2025 ASEE Southeast Conference , Mississippi State University, Mississippi, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/54140 Internet.  13 Jun, 2025

\bibitem{asee_peer_54140}
  Razvan Cristian Voicu, Muhammad Hassan Tanveer, and Ayse Tekes.
  "AI-Human Transference Learning and Assessment: Optimizing Knowledge Transfer and Understanding through AI-Generated Contextualization".
  \emph{2025 ASEE Southeast Conference , Mississippi State University, Mississippi, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/54140 Internet.  13 Jun, 2025

@INPROCEEDINGS{asee_peer_54140
author = "Razvan Cristian Voicu, Muhammad Hassan Tanveer, and Ayse Tekes"
title = "AI-Human Transference Learning and Assessment: Optimizing Knowledge Transfer and Understanding through AI-Generated Contextualization"
booktitle = "2025 ASEE Southeast Conference "
year = "2025"
month = "March"
address = "Mississippi State University, Mississippi"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/54140}
number = {10.18260/1-2--54140}
}

TY  - CPAPER
AB  - This paper presents a unique framework for AI-human transference learning, leveraging AI systems to facilitate effective knowledge and experience transfer, thereby improving learning outcomes. The proposed method utilizes AI-generated content, synthesized from multiple sources, to deliver contextual and task-specific knowledge while simultaneously integrating the transference of experiential learning derived from AI systems operating across diverse environments. This approach encompasses the development of adaptive learning materials, dynamic presentation formats, and optimized pathways tailored to individual learning needs. Additionally, the AI system employs continuous assessment inquiries to evaluate comprehension and provide personalized feedback, ensuring a deeper understanding of the content. The study further explores the potential of AI systems to transfer experiences gained from varied locations and contexts—ranging from terrestrial environments to space and beyond—into practical, human-applicable knowledge. By enabling cross-contextual experience sharing, the framework supports a future where AI and robotic systems enhance human learning by aggregating real-world experiences across different settings. The findings offer insights into the future of AI-driven education, where AI not only serves as a repository of knowledge but also as an agent for the transference of diverse experiences, thus supporting learners across various disciplines.
AU  - Razvan Cristian Voicu
AU  - Muhammad Hassan Tanveer
AU  - Ayse Tekes
CY  - Mississippi State University, Mississippi
DA  - 2025/03/09
PB  - ASEE Conferences
TI  - AI-Human Transference Learning and Assessment: Optimizing Knowledge Transfer and Understanding through AI-Generated Contextualization
UR  - https://peer.asee.org/54140
DO  - 10.18260/1-2--54140
ER  -