Effects of Using Artificial Intelligence Teaching System for Environmental Education on Environmental Knowledge and Attitude
Open accessAbstract
The emergence of computers resulted in the application revolution to instruction; till the emergence of the Internet, the strong communication ability became the major role and fully developed the integration of technology and network. The emergence of artificial intelligence teaching systems really fulfilled leaner-centered learning. Based on learner needs, the design changed the learning interaction in automatic teaching from the interaction with machines to the interaction with knowledge. With quasi-experimental study, total 186 college students, as the research object, are proceeded 16-week (3 hours per week for total 48 hours) environmental education with artificial intelligence teaching systems. The research results conclude significant correlations between 1.environmental education and environmental knowledge, 2.environmental knowledge and environmental attitude, and 3.environmental education and environmental attitude. According to the results, suggestions are proposed, expecting to reinforce the teaching ability of environmental education, cultivate college students’ understanding of environment, enhance environmental protection knowledge, attitude, and action intention, as well as promote the skill to use environmental action strategies.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Authors conceptualise AI as intelligent computer systems or intelligent agents with human features, such as the ability to memorise knowledge, to perceive and manipulate their environment in a similar way as humans, and to understand human natural language.
“The authors conceptualise AI as intelligent computer systems or intelligent agents with human features, such as the ability to memorise knowledge, to perceive and manipulate their environment in a similar way as humans, and to understand human natural language (see Huang, 2018;Lodhi, Mishra, Jain, & Bajaj, 2018;Welham, 2008).”
From Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019) · cites this paper as Huang, 2018
Cites
Mobile-based assessment: Investigating the factors that influence behavioral intention to use
Nikou, S. A., & Economides, A. A. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.02.005 · Cited by 388 · see citing works
What if pupils can assess their peers anonymously? A quasi-experimental study
Vanderhoven, E., Raes, A., Montrieux, H., Rotsaert, T., & Schellens, T. (2015) · Computers & Education · doi:10.1016/j.compedu.2014.10.001 · Cited by 96 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
“…intelligent computer systems or intelligent agents with human features, such as the ability to memorise knowledge, to perceive and manipulate their environment in a similar way as humans, and to understand human natural language (see Huang, 2018; Lodhi, Mishra, Jain, & Bajaj, 2018; Welham, 2008).”
BackgroundChiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100118 · Cited by 1,181 · see citing works
Chiu, T. K. F. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2253861 · Cited by 852 · see citing works
“The reviewed studies also showed that AI-enabled feed-back learning systems improved students problem-solving skills (Mokmin, 2020), communication skills (Hill et al., 2015), self-directed learning capacity (RodrÃguez et al., 2021), and creativity (Huang, 2018).”
Kim, J., Lee, H., & Cho, Y. H. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10831-6 · Cited by 669 · see citing works
Educational Collaboration : Teachers and Artificial Intelligence
Mujiono, M. (2023) · Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran · doi:10.33394/jk.v9i2.7801 · Cited by 12 · see citing works
- Authors
- Huang, S.-P.
- Affiliations
- China University of Science and Technology (TW)
- Corresponding author
- Huang, Shien-Ping
- Venue
- EURASIA Journal of Mathematics, Science and Technology Education
- Publisher
- Modestum Publishing Ltd
- Published
- 2018-05-16
- Volume
- 14
- Issue
- 7
- ISSN
- 1305-8215 (ISSN-L)
- 1305-8223
- OpenAlex
- W2803937896
- Cited by
- 67
- References
- 2
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 16 · 2023: 6 · 2022: 9 · 2021: 4 · 2020: 2 · 2019: 3
- FWCI
- 3.63
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Eurasia Journal of Mathematics Science and Technology Education (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental and sustainability education
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Education and Sustainability
- Keywords
- environmental education, environmental knowledge, environmental attitudes, university students, student-centered learning, educational technology, environmental protection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
