CoChat
Sign Up
Cited by 298Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationIntelligent Tutoring Systems and Adaptive Learning

Teacher’s Perceptions of Using an Artificial Intelligence-Based Educational Tool for Scientific Writing

Open access
Kim, Nam Ju, Kim, Min Kyu·2022·Frontiers in Education
DOI 10.3389/feduc.2022.755914
Open this paper in CoChat — free accountOpen PDF

Abstract

Efforts have constantly been made to incorporate AI into teaching and learning; however, the successful implementation of new instructional technologies is closely related to the attitudes of the teachers who lead the lesson. Teachers’ perceptions of AI utilization have only been investigated by only few scholars due an overall lack of experience of teachers regarding how AI can be utilized in the classroom as well as no specific idea of what AI-adopted tools would be like. This study investigated how teachers perceived an AI-enhanced scaffolding system developed to support students’ scientific writing for STEM education. Results revealed that most STEM teachers positively experienced AI as a source for superior scaffolding. On the other hand, they also raised the possibility of several issues caused by using AI such as the change in the role played by the teachers in the classroom and the transparency of the decisions made by the AI system. These results can be used as a foundation for which to create guidelines for the future integration of AI with STEM education in schools, since it reports teachers’ experiences utilizing the system and various considerations regarding its implementation.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/feduc.2022.755914

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 83 · Cited by 298

Research works printed but not linked: 62

  1. Anderson, J. R., Matessa, M., and Lebiere, C. (1997). ACT-R: a theory of higher level cognition and its relation to visual attention. Hum. Comput. Interact. 12, 439-462. doi: 10.1207/s15327051hci1204_5 · DOI
  2. Beal, C. R. (2013). "AnimalWatch: An intelligent tutoring system for algebra readiness, " in International Handbook of Metacognition and Learning Technologies, eds R. Azevedo and V. Aleven (New York: Springer), 337-348. doi: 10.1007/978-1-4419-5546-3_22 · DOI
  3. Becker, S. A., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V., et al. (2018). NMC Horizon Report: 2018 Higher Education Edition. Louisville: Educause.
  4. Belland, B. R. (2014). "Scaffolding: Definition, current debates, and future directions, " in Handbook of Research on Educational Communications and Technology, eds J. Spector, M. Merrill, J. Elen, and M. Bishop (Springer: New York), 505-518. doi: 10.1007/978-1-4614-3185-5_39 · DOI
  5. Belland, B. R., and Kim, N. J. (2021). Predicting high school students' argumentation skill using information literacy and trace data. J. Educ. Res. 114, 211-221. doi: 10.1080/00220671.2021.1897967 · DOI
  6. Belland, B. R., Gu, J., Kim, N. J., Jaden Turner, D., and Mark Weiss, D. (2019). Exploring epistemological approaches and beliefs of middle school students in problem-based learning. J. Educ. Res. 112, 643-655. doi: 10.1080/00220671. 2019.1650701 · DOI
  7. Belland, B. R., Weiss, D. M., and Kim, N. J. (2020). High school students' agentic responses to modeling during problem-based learning. J. Educ. Res. 113, 374- 383. doi: 10.1080/00220671.2020.1838407 · DOI
  8. Butz, B. P., Duarte, M., and Miller, S. M. (2006). An intelligent tutoring system for circuit analysis. IEEE Trans. Educ. 49, 216-223. doi: 10.1109/TE.2006.87 2407 · DOI
  9. Chang, C. Y., Hwang, G. J., and Gau, M. L. (2021). Promoting students' learning achievement and self-efficacy: a mobile chatbot approach for nursing training. Br. J. Educ. Technol. 53, 171-188. doi: 10.1111/bjet.13158 · DOI · Paper page
  10. Chong, S. X., and Lee, C.-S. (2012). Developing a Pedagogical-Technical Framework to Improve Creative Writing. Educ. Technol. Res. Dev. 60, 639-657. doi: 10.1007/s11423-012-9242-9 · DOI
  11. Cukurova, M., Khan-Galaria, M., Millán, E., and Luckin, R. (2021). A Learning Analytics Approach to Monitoring the Quality of Online One-to-one Tutoring. J. Learn. Anal. 1, 1-10. doi: 10.35542/osf.io/qfh7z · DOI
  12. Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., and Qin, J. (2020). Promoting students' well-being by developing their readiness for the artificial intelligence age. Sustainability 12:6597. doi: 10.3389/fdgth.2021.739327 · DOI
  13. D'Mello, S., and Graesser, A. (2012). Dynamics of affective states during complex learning. Learn. Instr. 22, 145-157. doi: 10.1016/j.learninstruc.2011.10.001 · DOI
  14. Doo, M. Y., Bonk, C., and Heo, H. (2020). A meta-analysis of scaffolding effects in online learning in higher education. Int. Rev. Res. Open Distrib. Learn. 21, 60-80. doi: 10.19173/irrodl.v21i3.4638 · DOI
  15. Doo, M. Y., Bonk, C. J., and Kim, J. (2021). An investigation of under-represented MOOC populations: motivation, self-regulation and grit among 2-year college students in Korea. J. Comput. High. Educ. 33, 1-22. doi: 10.1007/s12528-021- 09270-6 · DOI
  16. Fernández-Batanero, J. M., Román-Graván, P., Reyes-Rebollo, M. M., and Montenegro-Rueda, M. (2021). Impact of educational technology on teacher stress and anxiety: a literature review. Int. J. Environ. Res. Public Health 18:548. doi: 10.3390/ijerph18020548 · DOI
  17. Froemming, C., and Cifuentes, L. (2020). "Professional Development for Technology Integration in the Early Elementary Grades, " in Society for Information Technology & Teacher Education International Conference, (Waynesville: Association for the Advancement of Computing in Education (AACE)), 444-450.
  18. Gilakjani, A. P., Leong, L. M., and Ismail, H. N. (2013). Teachers' Use of Technology and Constructivism. Int. J. Mod. Educ. Comput. Sci. 5, 49-63. doi: 10.5815/ ijmecs.2013.04.07 · DOI
  19. González-Gómez, D., and Jeong, J. S. (2019). EdusciFIT: a computer-based blended and scaffolding toolbox to support numerical concepts for flipped science education. Educ. Sci. 9:116. doi: 10.3390/educsci9020116 · DOI
  20. Grogan, K. E. (2021). Writing Science: what Makes Scientific Writing Hard and How to Make It Easier. Bull. Ecol. Soc. Am. 102:e01800. doi: 10.1002/bes2.1800 · DOI
  21. Hao, Y., and Lee, K. S. (2015). Teachers' concern about integrating Web 2.0 technologies and its relationship with teacher characteristics. Comput. Hum. Behav. 48, 1-8. doi: 10.1016/j.chb.2015.01.028 · DOI
  22. Hébert, C., Jenson, J., and Terzopoulos, T. (2021). "Access to technology is the major challenge": teacher perspectives on barriers to DGBL in K-12 classrooms. E-Learn. Digital Media 18, 307-324. doi: 10.1177/2042753021995315 · DOI
  23. Hwang, G. J., and Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: a bibliometric mapping analysis and systematic review. Mathematics 9:584. doi: 10.3390/math9060584 · DOI
  24. Istenic, A., Bratko, I., and Rosanda, V. (2021). Pre-service teachers' concerns about social robots in the classroom: a model for development. Educ. Self Dev. 16, 60-87. doi: 10.26907/esd.16.2.05 · DOI
  25. Jeon, A. J., Kellogg, D., Khan, M. A., and Tucker-Kellogg, G. (2021). Developing critical thinking in STEM education through inquiry-based writing in the laboratory classroom. Biochem. Mol. Biol. Educ. 49, 140-150. doi: 10.1002/bmb. 21414 · DOI
  26. Jia, J., He, Y., and Le, H. (2020). "A multimodal human-computer interaction system and its application in smart learning environments, " in International Conference on Blended Learning, (Cham: Springer), 3-14. doi: 10.1007/978-3- 030-51968-1_1 · DOI
  27. Kaban, A. L., and Ergul, I. B. (2020). "Teachers' attitudes towards the use of tablets in six EFL classrooms, " in Examining the Roles of Teachers and Students in Mastering New Technologies, ed. P. Eva (Pennsylvania: IGI Global), 284-298. doi: 10.4018/978-1-7998-2104-5. · DOI
  28. ch015 · DOI
  29. Kim, J., Merrill, K., Xu, K., and Sellnow, D. D. (2020). My teacher is a machine: understanding students' perceptions of AI teaching assistants in online education. Int. J. Hum. Comput. Interact. 36, 1902-1911. doi: 10.1080/ 10447318.2020.1801227 · DOI · Paper page
  30. Kim, N. J., Belland, B. R., Lefler, M., Andreasen, L., Walker, A., and Axelrod, D. (2020). Computer-based scaffolding targeting individual versus groups in problem-centered instruction for STEM education: meta-analysis. Educ. Psychol. Rev. 32, 415-461. doi: 10.1007/s10648-019-09502-3 · DOI
  31. Kim, N. J., Vicentini, C. R., and Belland, B. R. (2021). Influence of scaffolding on information literacy and argumentation skills in virtual field trips and problem-based learning for scientific problem solving. Int. J. Sci. Math. Educ. 20, 215-236. doi: 10.1007/s10763-020-10145-y · DOI
  32. Lindsay, D. (2020). Scientific writing= thinking in words. Clayton: Csiro Publishing. doi: 10.1071/9781486311484 · DOI
  33. Luckin, R., Holmes, W., Griffiths, M., and Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. London: Pearson Education.
  34. Mercader, C., and Gairín, J. (2020). University teachers' perception of barriers to the use of digital technologies: the importance of the academic discipline. Int. J. Educ. Technol. High. Educ. 17:4. doi: 10.1186/s41239-020-0182-x · DOI
  35. Mitrović, A. (1998). "Experiences in implementing constraint-based modeling in SQL-Tutor, " in International Conference on Intelligent Tutoring Systems, (Berlin: Springer), 414-423. doi: 10.1007/3-540-68716-5_47 · DOI
  36. Moon, A., Gere, A. R., and Shultz, G. V. (2018). Writing in the STEM classroom: faculty conceptions of writing and its role in the undergraduate classroom. Sci. Educ. 102, 1007-1028. doi: 10.1002/sce.21454 · DOI
  37. Panigrahi, C. M. A. (2020). Use of artificial intelligence in education. Manage. Account. 55, 64-67. doi: 10.1371/journal.pone.0229596 · DOI · Paper page
  38. Proske, A., Narciss, S., and McNamara, D. S. (2012). Computer-based scaffolding to facilitate students' development of expertise in academic writing. J. Res. Read. 35, 136-152. doi: 10.1111/j.1467-9817.2010.01450.x · DOI
  39. Puri, N., and Mishra, G. (2020). "Artificial Intelligence (AI) in Classrooms: The Need of the Hour, " in Transforming Management Using Artificial Intelligence Techniques, eds V. Garg and R. Agrawal (Boca Raton: CRC Press), 169-183. doi: 10.1201/9781003032410-12 · DOI
  40. Qin, F., Li, K., and Yan, J. (2020). Understanding user trust in artificial intelligence- based educational systems: evidence from China. Br. J. Educ. Technol. 51, 1693-1710. doi: 10.1111/bjet.12994 · DOI · Paper page
  41. Reiser, B. J. (2004). Scaffolding complex learning: the mechanisms of structuring and problematizing student work. J. Learn. sci. 13, 273-304. doi: 10.1207/ s15327809jls1303_2 · DOI · Paper page
  42. Roll, I., and Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. Int. J. Artif. Intell. Educ. 26, 582-599. doi: 10.1007/s40593-016- 0110-3 · DOI · Paper page
  43. Ryu, M., and Han, S. (2018). The educational perception on artificial intelligence by elementary school teachers. J. Korean Assoc. Inform. Educ. 22, 317-324. doi: 10.1016/j.ridd.2019.01.002 · DOI
  44. Sakulkueakulsuk, B., Witoon, S., Ngarmkajornwiwat, P., Pataranutaporn, P., Surareungchai, W., Pataranutaporn, P., et al. (2018). "Kids making AI: Integrating machine learning, gamification, and social context in STEM education, " in 2018 IEEE international conference on teaching, assessment, and learning for engineering (TALE), (Wollongong: IEEE), 1005-1010. doi: 10.1109/ TALE.2018.8615249 · DOI
  45. Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R., and García Peñalvo, F. J. (2020). Assessed by Machines: development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students. Int. J. Interact. Multi. Artif. Intell. 6:80. doi: 10.9781/ijimai.2020.11.009 · DOI
  46. Saputri, A. A. (2021). Student science process skills through the application of computer based scaffolding assisted by PhET simulation. At-Taqaddum 13, 21-38. doi: 10.21580/at.v13i1.8151 · DOI
  47. Semerci, A., and Aydin, M. K. (2018). Examining high school teachers' attitudes towards ICT use in education. Int. J. Progress. Educ. 14, 93-105. doi: 10.29329/ ijpe.2018.139. · DOI
  48. Spain, R., Rowe, J., Smith, A., Goldberg, B., Pokorny, R., Mott, B., et al. (2021). A reinforcement learning approach to adaptive remediation in online training. J. Def. Model. Simul. doi: 10.1177/15485129211028317 · DOI
  49. Sperling, A., and Lickerman, D. (2012). "Integrating AI and machine learning in software engineering course for high school students, " in Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education, (New York: Association for Computing Machinery), 244-249. doi: 10.1145/2325296.2325354 · DOI
  50. Supriyadi, S. (2021). Evaluation instrument development for scientific writing instruction with a constructivism approach. Tech. Soc. Sci. J. 21, 345-363. doi: 10.47577/tssj.v21i1.3877 · DOI
  51. Tallvid, M. (2016). Understanding teachers' reluctance to the pedagogical use of ICT in the 1: 1 classroom. Educ. Inf. Technol. 21, 503-519. doi: 10.1007/s10639- 014-9335-7 · DOI
  52. Tan, S. C. (2000). Supporting Collaborative Problem Solving Through Computer- Supported Collaborative Argumentation. Unpublished Doctoral Dissertation. University Park, PA: The Pennsylvania State University.
  53. Toth, E. E., Suthers, D. D., and Lesgold, A. M. (2002). "Mapping to know": the effects of representational guidance and reflective assessment on scientific inquiry. Sci. Educ. 86, 264-286. doi: 10.1002/sce.10004 · DOI
  54. Trujillo-Torres, J. M., Hossein-Mohand, H., Gómez-García, M., Hossein-Mohand, H., and Cáceres-Reche, M. P. (2020). Mathematics teachers' perceptions of the introduction of ICT: the relationship between motivation and use in the teaching function. Mathematics 8:2158. doi: 10.3390/math8122158 · DOI
  55. Tshuma, N. (2021). The vulnerable insider: navigating power, positionality and being in educational technology research. Learn. Media Technol. 46, 218-229. doi: 10.1080/17439884.2021.1867572 · DOI
  56. Tucker, C., Jackson, K., and Park, J. J. (2020). "Exploring the future of engineering education: Perspectives from a workshop on artificial intelligence and the future of STEM and societies, " in ASEE Annual Conference and Exposition, Conference Proceedings, (Washington: American Society of Engineering Education).
  57. Vachovsky, M. E., Wu, G., Chaturapruek, S., Russakovsky, O., Sommer, R., and Fei-Fei, L. (2016). "Toward more gender diversity in CS through an artificial intelligence summer program for high school girls, " in Proceedings of the 47th ACM Technical Symposium on Computing Science Education, (Memphis: Association for Computing Machinery), 303-308. doi: 10.1145/ 2839509.2844620 · DOI
  58. Walker, A. S. (2019). Perusall: harnessing AI robo-tools and writing analytics to improve student learning and increase instructor efficiency. J. Writ. Anal. 3, 227-263. doi: 10.37514/JWA-J.2019.3.1.11 · DOI
  59. Wood, E., Mueller, J., Willoughby, T., Specht, J., and Deyoung, T. (2005). Teachers' perceptions: barriers and supports to using technology in the classroom. Educ. Commun. Inf. 5, 183-206. doi: 10.1080/14636310500186214 · DOI
  60. Yurtseven Avci, Z., O'Dwyer, L. M., and Lawson, J. (2020). Designing effective professional development for technology integration in schools. J. Comput. Assist. Learn. 36, 160-177. doi: 10.1111/jcal.12394 · DOI
  61. Zhai, X., Haudek, K. C., Shi, L., Nehm, R. H., and Urban-Lurain, M. (2020). From substitution to redefinition: a framework of machine learning-based science assessment. J. Res. Sci. Teach. 57, 1430-1459. doi: 10.1002/tea.21658 · DOI
  62. Zimmerman, J. (2006). Why some teachers resist change and what principals can do about it. NASSP Bull. 90, 238-249. doi: 10.1177/019263650629 1521 · DOI

Authors
Kim, N. J., & Kim, M. K.
Affiliations
  • University of Miami (US)
  • Georgia State University (US)
Corresponding author
Kim, Nam Ju
ORCID
Venue
Frontiers in Education
Publisher
Frontiers Media SA
Published
2022-03-29
Volume
7
ISSN
2504-284X (ISSN-L)
DOI
10.3389/feduc.2022.755914
OpenAlex
W4221026254
Cited by
298
References
83
Citations by year
2026: 76 · 2025: 106 · 2024: 90 · 2023: 25 · 2022: 1
FWCI
54.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
teacher perceptions, artificial intelligence in education, scientific writing, STEM education, teacher role, AI transparency, AI adoption in schools, writing support tools

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