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How to Measure Teachers' Acceptance of AI-driven Assessment in eLearning: A TAM-based Proposal

Authors:

José Carlos Sánchez-Prieto,

Juan Cruz-Benito,

Roberto Therón,

Francisco J. García-PeñalvoAuthors Info & Claims

TEEM'19: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

Pages 181 - 186

https://doi.org/10.1145/3362789.3362918

Published: 16 October 2019 Publication History

Abstract

The use of AI is becoming a growing reality the educational field. One of the activities in which it is beginning to be implemented is the assessment of student achievement. This way, we can find in the literature an increasing number of investigations focused on the possibilities offered by the adoption of AI-driven assessment. However, the use of AI is also a source of concern that raises suspicions in some sectors of our society. In this context, knowing the position of the teachers towards this topic is critical to guarantee the successful development of the field.

This paper intends to fill a research gap in the literature by offering a technology adoption model based on TAM to study the factors that condition the use of AI-driven assessment among teachers. To present this model we offer a background on the use of AI in education and the technology acceptance among teachers, as well as the definition of the eight constructs and the relational hypotheses included. Finally, the possibilities of the model and future lines of research are discussed.

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Cited By

Bölükbaş FBudak KPolat EEngin KUlutaş İ(2024)Artificial Intelligence Experiences of Pre-Service Preschool TeachersIntegrating Artificial Intelligence in Education10.4018/979-8-3693-3944-2.ch003(59-88)Online publication date: 16-Aug-2024
https://doi.org/10.4018/979-8-3693-3944-2.ch003
Meylani R(2024)Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research LiteratureJournal of Computer and Education Research10.18009/jcer.147770912:24(600-637)Online publication date: 21-Oct-2024
https://doi.org/10.18009/jcer.1477709
Ma SLei L(2024)The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teachingAsia Pacific Journal of Education10.1080/02188791.2024.230515544:1(94-111)Online publication date: 16-Jan-2024
https://doi.org/10.1080/02188791.2024.2305155
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Index Terms

How to Measure Teachers' Acceptance of AI-driven Assessment in eLearning: A TAM-based Proposal
1. Applied computing
  1. Education
    1. Computer-assisted instruction
2. Social and professional topics
  1. User characteristics

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TEEM'19: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

October 2019

1085 pages

ISBN:9781450371919

DOI:10.1145/3362789

Editors:
Miguel Ángel Conde González
Universidad de León
,
Francisco Jesús Rodríguez Sedano
Universidad de León
,
Camino Fernández Llamas
Universidad de León
,
Francisco José García-Peñalvo
Universidad de Salamanca

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 16 October 2019

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TEEM'19

TEEM'19: Technological Ecosystems for Enhancing Multiculturality

October 16 - 18, 2019

León, Spain

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https://doi.org/10.4018/979-8-3693-3944-2.ch003
Meylani R(2024)Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research LiteratureJournal of Computer and Education Research10.18009/jcer.147770912:24(600-637)Online publication date: 21-Oct-2024
https://doi.org/10.18009/jcer.1477709
Ma SLei L(2024)The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teachingAsia Pacific Journal of Education10.1080/02188791.2024.230515544:1(94-111)Online publication date: 16-Jan-2024
https://doi.org/10.1080/02188791.2024.2305155
Ayanwale MAdelana OOdufuwa T(2024)Exploring STEAM teachers’ trust in AI-based educational technologies: a structural equation modelling approachDiscover Education10.1007/s44217-024-00092-z3:1Online publication date: 26-Apr-2024
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Bohara DRana K(2024)Unmasking teachers’ proficiency in harnessing Artificial Intelligence (AI) for transformative educationSN Social Sciences10.1007/s43545-024-01003-74:11Online publication date: 28-Oct-2024
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Zhang CSchießl JPlößl LHofmann FGläser-Zikuda M(2023)Acceptance of artificial intelligence among pre-service teachers: a multigroup analysisInternational Journal of Educational Technology in Higher Education10.1186/s41239-023-00420-720:1Online publication date: 4-Sep-2023
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Thakare RHasabnis HBarsainya SSurkar VPatil R(2023)Automative Feedback System for Descriptive Answers in LMS using Machine Learning Algortihm2023 IEEE Pune Section International Conference (PuneCon)10.1109/PuneCon58714.2023.10450028(1-6)Online publication date: 14-Dec-2023
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Pangarkar SPaigude S(2023)Artificial intelligence based analysis of student’s involvement in edtech industryPROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ENGINEERING RESEARCH AND APPLICATION 2022 (ICERA 2022)10.1063/5.0175884(020043)Online publication date: 2023
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Arthur Y(2022)MATHEMATICS TEACHERS’ ACCEPTANCE OF ICT IN TEACHING AND LEARNING: AN EXTENDED TECHNOLOGY ACCEPTANCE MODELProblems of Education in the 21st Century10.33225/pec/22.80.40880:3(408-425)Online publication date: 25-Jun-2022
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