ArticleEducation and information technologies2023
An integrated model for predicting pupils' acceptance of artificially intelligent robots as teachers.
Article in Education and information technologies, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Development and implementation of a multifunctional mobile robot training kit in embedded control systems instruction in vocational education.HardwareX · 2026Article
- University students' acceptance of generative artificial intelligence tools: a mixed-methods study on opinions, attitudes, and behavioral intentions.BMC psychology · 2026Article
- Profiling public perception of emerging technologies: Gene editing, brain chips and exoskeletons. A data-analytics framework.Heliyon · 2024Article
- Examining customer intentions to purchase intelligent robotic products and services in Taiwan using the theory of planned behaviour.BMC psychology · 2024Article
- Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificially intelligent robots as teachers (AI teachers) have attracted extensive attention due to their potential to relieve the challenge of global teacher shortage and realize universal elementary education by 2030. Despite mass production of service robots and discussions about their educational applications, the study of full-fledged AI teachers and children's attitudes towards them is quite preliminary. Here, we report a new AI teacher and an integrated model to assess how pupils accept and use it. Participants included students from Chinese elementary schools via convenience sampling. Questionnaires (n = 665), descriptive statistics and structural equation modeling based on software SPSS Statistics 23.0 and Amos 26.0 were carried out in data collection and analysis. This study first developed an AI teacher by coding a lesson design, course contents and Power Point with script language. Based on the popular Technology Acceptance Model and Task-Technology Fit Theory, this study identified key determinants of the acceptance, including robot use anxiety (RUA), perceived usefulness (PU), perceived ease of use (PEOU) and robot instructional task difficulty (RITD). Moreover, this study found that pupils' attitudes towards the AI teacher, which could be predicted by PU, PEOU and RITD, were generally positive. It is also found that the relationship between RITD and acceptance was mediated by RUA, PEOU and PU. This study holds significance for stakeholders to develop independent AI teachers for students.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.