Leveraging Artificial Intelligence with Zone of Proximal Development: An ARCS Motivational E-Learning Model

Matilda Isaac; Mohammad Ateeq; Hadyan Hafizh; Bintao Hu; Dolapo Shodipo

doi:10.1109/TALE56641.2023.10398344

Leveraging Artificial Intelligence with Zone of Proximal Development: An ARCS Motivational E-Learning Model

Matilda Isaac, Mohammad Ateeq, Hadyan Hafizh, Bintao Hu^*, Dolapo Shodipo

^*Corresponding author for this work

School of Internet of Things

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

The study aims to investigate the complex effects of Artificial Intelligence (AI) in Education (AIED) on Zone of Proximal Development (ZPD). ZPD a concept introduced by Lev Vygotsky during the late 1920's, stands on the premise that the distance between actual development level (independence) and the level of potential development (dependence) lies in mentorship by adult guidance and peer collaboration. Several social-cultural theories have utilized this to analyze the relationship between instructor and learner achievement. The idea is to influence the learner's knowledge zone by active and productive interaction. It has been noted that when learners are given the opportunity to collaborate it not only motivates the learner but also influences their sociological development and this helps to bolster their already established knowledge. However, the role of AI on the ZPD of the learner creates a dichotomy that needs further exploration. A ubiquitous AI educational setting may encourage learners to remain passive rather than foster participatory behaviour and positive outcome. These inclinations toward passive compliance are evident in the concept of AI systems enabling"personalized learning,"which departs from the theory of learning as a social action and civic engagement. We further explore the role of motivation through the ARCS model in minimizing an adverse effect of AI on a learner's ZPD.

Original language	English
Title of host publication	2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665453318
DOIs	https://doi.org/10.1109/TALE56641.2023.10398344
Publication status	Published - 2023
Event	2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Auckland, New Zealand Duration: 28 Nov 2023 → 1 Dec 2023

Publication series

Name	2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings

Conference

Conference	2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023
Country/Territory	New Zealand
City	Auckland
Period	28/11/23 → 1/12/23

Keywords

ARCS
Artificial intelligence
Computer-based training
E-Learning
Precision learning
ZPD

Access to Document

10.1109/TALE56641.2023.10398344

Cite this

Isaac, M., Ateeq, M., Hafizh, H., Hu, B., & Shodipo, D. (2023). Leveraging Artificial Intelligence with Zone of Proximal Development: An ARCS Motivational E-Learning Model. In 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings (2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TALE56641.2023.10398344

Isaac, Matilda ; Ateeq, Mohammad ; Hafizh, Hadyan et al. / Leveraging Artificial Intelligence with Zone of Proximal Development : An ARCS Motivational E-Learning Model. 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings).

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abstract = "The study aims to investigate the complex effects of Artificial Intelligence (AI) in Education (AIED) on Zone of Proximal Development (ZPD). ZPD a concept introduced by Lev Vygotsky during the late 1920's, stands on the premise that the distance between actual development level (independence) and the level of potential development (dependence) lies in mentorship by adult guidance and peer collaboration. Several social-cultural theories have utilized this to analyze the relationship between instructor and learner achievement. The idea is to influence the learner's knowledge zone by active and productive interaction. It has been noted that when learners are given the opportunity to collaborate it not only motivates the learner but also influences their sociological development and this helps to bolster their already established knowledge. However, the role of AI on the ZPD of the learner creates a dichotomy that needs further exploration. A ubiquitous AI educational setting may encourage learners to remain passive rather than foster participatory behaviour and positive outcome. These inclinations toward passive compliance are evident in the concept of AI systems enabling{"}personalized learning,{"}which departs from the theory of learning as a social action and civic engagement. We further explore the role of motivation through the ARCS model in minimizing an adverse effect of AI on a learner's ZPD.",

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author = "Matilda Isaac and Mohammad Ateeq and Hadyan Hafizh and Bintao Hu and Dolapo Shodipo",

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language = "English",

series = "2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings",

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Isaac, M , Ateeq, M, Hafizh, H, Hu, B & Shodipo, D 2023, Leveraging Artificial Intelligence with Zone of Proximal Development: An ARCS Motivational E-Learning Model. in 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings. 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023, Auckland, New Zealand, 28/11/23. https://doi.org/10.1109/TALE56641.2023.10398344

Leveraging Artificial Intelligence with Zone of Proximal Development: An ARCS Motivational E-Learning Model. / Isaac, Matilda ; Ateeq, Mohammad; Hafizh, Hadyan et al.
2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Hu, Bintao

AU - Shodipo, Dolapo

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AB - The study aims to investigate the complex effects of Artificial Intelligence (AI) in Education (AIED) on Zone of Proximal Development (ZPD). ZPD a concept introduced by Lev Vygotsky during the late 1920's, stands on the premise that the distance between actual development level (independence) and the level of potential development (dependence) lies in mentorship by adult guidance and peer collaboration. Several social-cultural theories have utilized this to analyze the relationship between instructor and learner achievement. The idea is to influence the learner's knowledge zone by active and productive interaction. It has been noted that when learners are given the opportunity to collaborate it not only motivates the learner but also influences their sociological development and this helps to bolster their already established knowledge. However, the role of AI on the ZPD of the learner creates a dichotomy that needs further exploration. A ubiquitous AI educational setting may encourage learners to remain passive rather than foster participatory behaviour and positive outcome. These inclinations toward passive compliance are evident in the concept of AI systems enabling"personalized learning,"which departs from the theory of learning as a social action and civic engagement. We further explore the role of motivation through the ARCS model in minimizing an adverse effect of AI on a learner's ZPD.

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Isaac M , Ateeq M, Hafizh H, Hu B, Shodipo D. Leveraging Artificial Intelligence with Zone of Proximal Development: An ARCS Motivational E-Learning Model. In 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings). doi: 10.1109/TALE56641.2023.10398344

Leveraging Artificial Intelligence with Zone of Proximal Development: An ARCS Motivational E-Learning Model

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