Measuring Students' Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model

Artificial Intelligence is one of the trend areas in research. It is applied in many different contexts successfully. One of the contexts where Artificial Intelligence is applied is in Education. In the literature, we find several works in the last years that explore the application of Artificial Intelligence-related techniques to analyze students’ behavior, to enable virtual tutors or to assess the learning. However, what are the students’ perceptions on this subject of Artificial Intelligence and Education? Do they accept the use of Artificial Intelligence techniques to assess their learning? Are they reluctant to be influenced by non-human agents in such a human process like education? To try to respond to these questions, this paper presents a novel proposal of a research model based on the Technology Acceptance Model. To describe the model, we present its different main constructs and variables, as well as the hypotheses to analyze, adapted to the object of study. Finally, we discuss the main implications of this research model, the opportunities that could come based on this proposal and the future of this research.

[1]  Paul A. Pavlou,et al.  Social identity and trust in internet-based voting adoption , 2018, Gov. Inf. Q..

[2]  Icek Ajzen,et al.  From Intentions to Actions: A Theory of Planned Behavior , 1985 .

[3]  Yue Guo,et al.  Antecedents of trust and continuance intention in mobile payment platforms: The moderating effect of gender , 2019, Electron. Commer. Res. Appl..

[4]  Francisco J. García-Peñalvo,et al.  Break the walls! Second-Order barriers and the acceptance of mLearning by first-year pre-service teachers , 2019, Comput. Hum. Behav..

[5]  Ido Roll,et al.  Evolution and Revolution in Artificial Intelligence in Education , 2016, International Journal of Artificial Intelligence in Education.

[6]  Michael J. Timms Letting Artificial Intelligence in Education Out of the Box: Educational Cobots and Smart Classrooms , 2016, International Journal of Artificial Intelligence in Education.

[7]  William R. King,et al.  A meta-analysis of the technology acceptance model , 2006, Inf. Manag..

[8]  Maria Samarakou,et al.  Implementation of artificial intelligence assessment in engineering laboratory education , 2014 .

[9]  Daniyal M. Alghazzawi,et al.  A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms , 2017, J. Artif. Intell. Soft Comput. Res..

[10]  Chi-Jen Lin,et al.  Redefining the learning companion: the past, present, and future of educational agents , 2003, Comput. Educ..

[11]  Peter Mikulecký,et al.  Smart Environments for Smart Learning , 2012 .

[12]  Tahir Cetin Akinci,et al.  Evaluation of student performance in laboratory applications using fuzzy logic , 2010 .

[13]  Krishna Moorthy,et al.  Is facebook useful for learning? A study in private universities in Malaysia , 2019, Comput. Educ..

[14]  Pandian Vasant,et al.  Handbook of Research on Artificial Intelligence Techniques and Algorithms , 2015 .

[15]  Ronald T. Cenfetelli Inhibitors and Enablers as Dual Factor Concepts in Technology Usage , 2004, J. Assoc. Inf. Syst..

[16]  Vladimir Stantchev,et al.  Cloud computing service for knowledge assessment and studies recommendation in crowdsourcing and collaborative learning environments based on social network analysis , 2015, Comput. Hum. Behav..

[17]  C. Flavián,et al.  How bricks‐and‐mortar attributes affect online banking adoption , 2006 .

[18]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[19]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..

[20]  Ronny Scherer,et al.  The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education , 2019, Comput. Educ..

[21]  Daniel L. Schwartz,et al.  Preparing students for future learning with Teachable Agents , 2010 .

[22]  Refik Caglar Kizilirmak,et al.  Predicting Students’ GPA and Developing Intervention Strategies Based on Self-Regulatory Learning Behaviors , 2017, IEEE Access.

[23]  Yongqiang Sun,et al.  The dark side of elderly acceptance of preventive mobile health services in China , 2013, Electron. Mark..

[24]  Christoph Peylo,et al.  W2 - Adaptive and Intelligent Web-Based Education Systems , 2003, Intelligent Tutoring Systems.

[25]  David J. McArthur,et al.  The roles of artificial intelligence in education : current progress and future prospects: , 2005 .

[26]  Simon Holland,et al.  Artificial intelligence, education and music: the use of artificial intelligence to encourage and facilitate music composition by novices , 1989 .

[27]  Ron Stevens,et al.  The Use of Artificial Neural Nets (ANN) to Help Evaluate Student Problem Solving Strategies , 2000 .

[28]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[29]  Timothy Teo,et al.  Factors that influence university students’ intention to use Moodle: a study in Macau , 2019, Educational Technology Research and Development.

[30]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[31]  Ángel Hernández García Desarrollo de un modelo unificado de adopción del comercio electrónico entre empresas y consumidores finales. Aplicación al mercado español , 2012 .

[32]  Jeremy Straub,et al.  An expert system for the prediction of student performance in an initial computer science course , 2017, 2017 IEEE International Conference on Electro Information Technology (EIT).

[33]  Begoña Gros,et al.  The Dialogue Between Emerging Pedagogies and Emerging Technologies , 2016 .

[34]  Analía Amandi,et al.  An enhanced Bayesian model to detect students' learning styles in Web-based courses , 2008, J. Comput. Assist. Learn..

[35]  Huaiqing Wang,et al.  Intelligent agent supported personalization for virtual learning environments , 2006, Decis. Support Syst..

[36]  Anastasios A. Economides,et al.  Prediction of student's mood during an online test using formula-based and neural network-based method , 2009, Comput. Educ..

[37]  Pedro J. Muñoz Merino,et al.  Learning analytics trends and challenges in engineering education: SNOLA special session , 2018, 2018 IEEE Global Engineering Education Conference (EDUCON).

[38]  Thomas B. Sheridan,et al.  Human–Robot Interaction , 2016, Hum. Factors.

[39]  Francisco J. García-Peñalvo,et al.  A Deep-Learning-Based Proposal to Aid Users in Quantum Computing Programming , 2018, HCI.

[40]  Francisco José García-Peñalvo,et al.  Enabling Adaptability in Web Forms Based on User Characteristics Detection Through A/B Testing and Machine Learning , 2018, IEEE Access.

[41]  Dale Goodhue,et al.  Task-Technology Fit and Individual Performance , 1995, MIS Q..

[42]  Wei Zhang,et al.  The roles of initial trust and perceived risk in public’s acceptance of automated vehicles , 2019, Transportation Research Part C: Emerging Technologies.

[43]  John Ingham,et al.  Why do people use information technology? A critical review of the technology acceptance model , 2003, Inf. Manag..

[44]  Sujeet Kumar Sharma,et al.  Neural network approach to predict mobile learning acceptance , 2018, Education and Information Technologies.

[45]  Hüseyin Çakir,et al.  Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review , 2015, ArXiv.

[46]  Tahir Islam,et al.  Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory , 2017, Telematics Informatics.

[47]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[48]  Etienne Wenger,et al.  Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge , 1987 .

[49]  Richard E. Mayer,et al.  Polite web-based intelligent tutors: Can they improve learning in classrooms? , 2011, Comput. Educ..

[50]  Yaping Zang,et al.  Advances of flexible pressure sensors toward artificial intelligence and health care applications , 2015 .

[51]  Brandford Bervell,et al.  QR code utilization in a large classroom: Higher education students’ initial perceptions , 2018, Education and Information Technologies.

[52]  Jie Hu,et al.  The Exploration of a Machine Learning Approach for the Assessment of Learning Styles changes , 2018, Mechatron. Syst. Control..

[53]  Hamidah Sulaiman,et al.  Factors influencing the rural students' acceptance of using ICT for educational purposes , 2018 .

[54]  Mingming Zhou,et al.  Chinese university students' acceptance of MOOCs: A self-determination perspective , 2016, Comput. Educ..

[55]  Manisha Sharma,et al.  Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation , 2019, Int. J. Inf. Manag..

[56]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[57]  S. Russel and P. Norvig,et al.  “Artificial Intelligence – A Modern Approach”, Second Edition, Pearson Education, 2003. , 2015 .

[58]  Mohamed A. Shahin,et al.  State-of-the-art review of some artificial intelligence applications in pile foundations , 2016 .

[59]  Manuel Contero,et al.  Study on Parent's Acceptance of the Augmented Reality Use for Preschool Education , 2013, VARE.

[60]  Siu-Ming Yiu,et al.  SmartTutor: An intelligent tutoring system in web-based adult education , 2003, J. Syst. Softw..

[61]  Anol Bhattacherjee,et al.  Physicians' resistance toward healthcare information technology: a theoretical model and empirical test , 2007, Eur. J. Inf. Syst..

[62]  Hyacinth S. Nwana,et al.  Intelligent tutoring systems: an overview , 1990, Artificial Intelligence Review.

[63]  John Self Artificial Intelligence and Human Learning: Intelligent Computer-Aided Instruction , 1988 .