Journal of Korean Library and Information Science Society (한국도서관정보학회지)

Korean Library and Information Science Society (한국도서관정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Intention to Use of the AI-related Educational Content Recommendation System in the University Library: Focusing on the Perceptions of University Students and Librarians

대학도서관 인공지능 관련 교육콘텐츠 추천 시스템 사용의도에 관한 연구 - 대학생과 사서의 인식을 중심으로 -

  • 김성훈 (성균관대학교 문헌정보학과) ;
  • 박시온 (성균관대학교 문헌정보학과) ;
  • 박지원 (성균관대학교 문헌정보학과) ;
  • 오유진 (성균관대학교 문헌정보학과)
  • Received : 2022.02.23
  • Accepted : 2022.03.14
  • Published : 2022.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

인공지능에 대한 이해 및 업무분야에서의 활용 능력은 지식 정보화 시대를 살아가는 모든 사람에게 기본 역량으로 강조되고 있으며, 이에 따라 인공지능에 대한 교육의 필요성은 대학 구성원들에게도 높게 인식되고 있다. 국내외 대학도서관 역시 효과적인 인공지능 콘텐츠 제공의 필요성을 인식하여 전자 형태의 디지털 콘텐츠를 제공하고 있으나, 인공지능이라는 정보 기술에 특화된 이용자 맞춤형 추천은 제공되고 있지 않고 있으며 이러한 추천서비스에 대한 이용자의 관심 파악 역시 미비하다. 대학생의 인공지능 교육에 대한 수요가 증가하고 있는 상황에서, 대학도서관에서의 인공지능 관련 콘텐츠 추천에 대한 이용자의 이용의사를 파악하고 효과적인 서비스 수립을 위한 조사가 절실히 필요한 시점이다. 본 연구는 확장된 기술수용모델을 활용하여 인공지능 주제 분야에 특화된 디지털 교육 콘텐츠를 추천해주는 서비스에 대한 이용자들의 사용의도에 영향을 주는 요인을 도출하였으며, 대학생을 대상으로 한 온라인 설문조사, 대학도서관 사서들과의 서면인터뷰를 통해 각 요인별 영향력을 조사하고, 성공적 수행을 위한 제언을 수렴하였다. 연구결과, 인공지능관련 교육콘텐츠 추천시스템 사용의도는 성별, 학년, 전공계열에 상관없이 사용의사가 있다고 조사되었고, 과제적합성요인이 사용의도에 가장 영향을 미치는 요인임이 파악되었다. 사서들 또한 서비스의 필요성을 깊이 공감하고 있었고 현실적인 제약사항으로 예산과 콘텐츠 품질 문제를 제시하였다.

Keywords

References

  1. Bao, Tantan & Kim, Hun (2021). Factors influencing the resistance of recommendation system in OTT service: focusing on the case of Netflix. Journal of Broadcasting and Telecommunications Research, 115, 9-46.
  2. Cho, Hyun Yang (2017). A experimental study on the development of a book recommendation system using automatic classification, based on the personality type. Journal of Korean Library and Information Science Society, 48(2), 215-236. https://doi.org/10.16981/KLISS.48.2.201706.215
  3. Cho, Hyun Yang (2020). Design of the curation platform for user-participated book recommendation system of selecting on alternative material for the disabled. Journal of the Korean Society for Library and Information Science, 54(3), 41-69. https://doi.org/10.4275/KSLIS.2020.54.3.041
  4. Cho, Soon Jeong (2015). Factors influencing the acceptance of massive open online courses: an explanatory study based on technology acceptance model II. The Journal of Learner-Centered Curriculum and Instruction, 15(9), 601-619.
  5. Choi, Hyuk Ra (2004). An empirical study on factors influencing the use intention of mobile internet under the mobile characteristics. Journal of Industrial Economics and Business, 17(4), 1399-1420.
  6. Chung, HeeChung & Cho, Sung-Bae (2011). Personalized recommendation service based on collaborative filtering for library information systems. Proceedings of the Korean Information Science Society Conference, 38(1A), 251-254.
  7. Ewha Womans University Library Book Profiling Services Now Includes E-Books! (2020, March 23). Ewha Womans University Library Official Blog, Available: https://ewhalibnews.tistory.com/777
  8. Han, Myung-Seong (2021). Analysis of the public's intention to use the government's artificial intelligence (AI)-based services: focusing on public values and extended technology acceptance model. The Journal of the Korea Contents Association, 21(8), 388-402. https://doi.org/10.5392/JKCA.2021.21.08.388
  9. Han, Sang Hoon (2002). The relationship between adult students' goal orientations and self-efficacy. Korean Journal of Educational Research, 40(6), 167-181.
  10. Hong, Yeon Kyoung, Jeon, Seo Young, Choi, Jae Young, Yang, Hee Yoon, Han, Chae Eun, & Zhu, Yong Jun (2021). A study on the development and evaluation of personalized book recommendation systems in university libraries based on individual loan records. Journal of the Korean Society for Information Management, 38(2), 113-127. https://doi.org/10.3743/KOSIM.2021.38.2.113
  11. Hwang, Seung-hee, Um, Ju-hyun, Sul, Hyun-ju, Lee, Min-young, & Yun, Jae Young (2017). Online-to-Offline service design to provide personalized information for university library users. Journal of Communication Design, 59, 388-397.
  12. Jeon, Young Mee & Cho, Jin Suk (2019). Factors influencing the university students' satisfaction and continuous use intention on K-MOOC. The Journal of the Korea Contents Association, 19(11), 80-91. https://doi.org/10.5392/JKCA.2019.19.11.080
  13. Jeong, Eun Seong, Choi, Sa Ra, & Son, Min Young (2021). A study on intention to use of smart tourism contents through extended technology acceptance model: case of visitors to the National Museum of Korea. Journal of Digital Convergence, 19(9), 115-123. https://doi.org/10.14400/JDC.2021.19.9.115
  14. Jeong, Han Ho (2017). A study of factors affecting continuous behavior intention of college students on MOOC-Based on TAM, ECM, and TFM. Journal of Korean Association for Educational Information and Media, 23(2), 315-343. https://doi.org/10.15833/KAFEIAM.23.2.315
  15. Jeong, Han Ho (2018). A study on the structural relationships among variables affecting adult learners' intention to take MOOC: based on social influence, self efficacy, expectation-confirmation, task-technology fit, and satisfaction. Journal of Lifelong Learning Society, 14(4), 203-229. https://doi.org/10.26857/jlls.2018.11.14.4.203
  16. Jeong, Han Ho (2020). A study on the degree of continuous participation in remote classes conducted in the Corona Era: based on ECM, TAM, TFM, and TRA. Journal of Education Inquiry, 33(4), 33-59.
  17. Jin, Min Ha, Jeong, Seung Yeon, Cho, Eun Ji, Lee, Myoung Hun, & Kim, Keun Wook (2021). Implementation of the unborrowed book recommendation system for public libraries: based on Daegu D library. Journal of Digital Convergence, 19(5), 175-186. https://doi.org/10.14400/JDC.2021.19.5.175
  18. Kang, Boo Sik (2021). Accuracy improvement of movie recommender system using. Journal of Knowledge Information Technology and Systems, 16(2), 255-263. https://doi.org/10.34163/JKITS.2021.16.2.002
  19. Kim, Bae Sung & Woo, Hyung Jin (2019). A study on the intention to use AI speakers: focusing on extended technology acceptance model. Journal of the Korea Contents Association, 19(9), 1-10. https://doi.org/10.5392/JKCA.2019.19.09.001
  20. Kim, Chung Il, Choi, Nam Gyu, Heo, Yu Jin, Sin, Ji Hoon, & Yoon, Jang Hyeok (2015). On the development of a course recommender system: a Hybrid Filtering approach. Entrue Journal of Information Technology, 14(2), 71-82.
  21. Kim, Dong Sim & Lee, Young-Sun (2018). Difference of information quality, service quality, system quality and satisfaction between university students and the general public in MOOC. Journal of Lifelong Learning Society, 14(2), 83-103. https://doi.org/10.26857/jlls.2018.5.14.2.83
  22. Kim, Doo Hyung, Shin, Woo Seok, Han, Ki Wung, Lee, Jin-Suk, Moon, Ki Bum, Lee, Su Kang, Han, Su Yeon, Kwon, Hye Jeong, & Han, Sung Won (2020). A system for recommending university liberal arts courses using collaborative filtering. Journal of the Korean Institute of Industrial Engineers Autumn Conference Proceeding, 11, 2551-2556.
  23. Kim, Gwang-Jae (2009). A study on acceptance factor of digital multimedia broadcasting. Korean Journal of Journalism & Communication Studies, 53(3), 296-323.
  24. Kim, Kyung-ihl (2014). An exploratory study on IMS performance modeling using information system success model. Journal of Digital Convergence, 12(3), 127-140. https://doi.org/10.14400/JDC.2014.12.3.127
  25. Kim, Su Youn & Park Inn Woo (2019). The structural relationship among intention to take, quality, learning satisfaction, achievement and continued to use intention in K-MOOC learning environment. Journal of Korean Association for Educational Information and Media, 25(3), 525-549. https://doi.org/10.15833/KAFEIAM.25.3.525
  26. Kim, Sung Ae & Park, Ju Yeon (2021). Artificial intelligence education status and implications at liberal arts education of universities. Journal of The Korean Association of Artificial Intelligence Education, 2(2), 31-38. https://doi.org/10.52618/aied.2021.2.2.4
  27. Kim, Sung Han & Leem, Choon Seong (2020). Factors affecting the transfer intention of vr construction safety training: a task-technology fit perspective. Global Business Administration Review, 17(3), 300-318. https://doi.org/10.38115/asgba.2020.17.3.300
  28. Kim, Sung Jin (2014). Factors influencing the intention to use mobile services in academic libraries. Journal of the Korean Biblia Society for Library and Information Science. 25(1), 85-105. https://doi.org/10.14699/KBIBLIA.2014.25.1.085
  29. Kim, Yeon Rye & Hong, Hyun Jin (2006). A study on the acceptance of information literacy instruction for school libraries in Korea. Journal of Korean Library and Information Science Society, 37(3), 3-32.
  30. Lee, Han Shin & Kim, Pan Soo (2019). The effect of consumer's technology acceptance and resistance on intention to use of artificial intelligence (AI). Korean Management Review, 48(5), 1195-1219. https://doi.org/10.17287/kmr.2019.48.5.1195
  31. Lee, Jae Shin (2006). A longitudinal study examining factors meeting changes of attitudes towards technology use: focusing on individuals' subjective judgements and social influence of technology use. Korean Journal of Journalism & Communication Studies, 50(6), 388-414.
  32. Lee, Jin Sook, Moon, Ki Bum, Han, Su Yeon, Lee, Su Kang, Kwon, Hye Jung, Han, Jae Ho, & Kim, Gyu Tae (2021). Development and application of an AI-Powered adaptive course recommender system in higher education: an example from K university. Journal of Educational Technology, 37(2), 267-307. https://doi.org/10.17232/KSET.37.2.267
  33. Lee, Jong-Won & Koh, Chan (2010). A study on activate methods for digital contents service to support academic courses in e-learning. Journal of Digital Convergence, 8(2), 89-102.
  34. Lee, Jong-Yeon & Lee, Eunjin (2010). Influence analysis of system, information and service qualities on learner satisfaction in university e-learning. Journal of Education Science, 41(3), 119-147.
  35. Lee, Jong-Yeon, Kim, Su-Uk, & Kim, Ju-Ri (2013). Analysis of quality factors influencing learner satisfaction on mobile learning linked to e-learning in universities. Journal of Educational Technology, 29(2), 209-240. https://doi.org/10.17232/KSET.29.2.209
  36. Lee, Min-Kyu, Kim, Won-Je, & Song, Min-Ho (2019). A study on the factors influencing continuous intention to use of OTT service users: focused on the extension of technology acceptance model. Journal of Digital Convergence, 17(11), 537-546. https://doi.org/10.14400/JDC.2019.17.11.537
  37. Lee, Woong Kyu (2007). The influence of security and risk perception on the reuse of internet banking. Asia Pacific Journal of Information Systems, 17(1), 77-93.
  38. Lee, Yong Jun & Kim, Won Je (2021). A study on factors affecting OTT users' intention to continue using curation services. Journal of Digital Convergence, 19(4), 217-225. https://doi.org/10.14400/JDC.2021.19.4.217
  39. Lee, Yong-Gu & Jung, Yeong-Mi (2002). Developing a book recommendation system using filtering techniques. Journal of Information Science Theory and Practice, 33(1), 1-17.
  40. Moon, Kyonghi, Yang, Jayoung, & Park, Seongho (2021). A study on the direction of AI liberal arts education based on the AI perceptions of freshmen in university. Korean Journal of General Education, 15(5), 11-23.
  41. Na, Jiyoung & Wui, Min-Young (2019). A study on the intention of using VR games: focusing on technology acceptance model(TAM). Journal of Korea Game Society, 19(3), 53-64. https://doi.org/10.7583/JKGS.2019.19.3.53
  42. Oh, Ji Hee (2021). A study on factors affecting the intention to use the Metaverse by applying the extended technology acceptance model(ETAM): focused on the virtual world Metaverse. The Journal of the Korea Contents Association, 21(10), 204-216. https://doi.org/10.5392/JKCA.2021.21.10.204
  43. Park, Daw-Woo, Koh, In Soo, Lee, Nak-Son, & Han, Kyeong-Seok (2020). A study on architecture for bigdata-based book curation system. Jounal of the Korea Society of Information Technology Policy & Management, 12(1), 1559-1565.
  44. Park, Hye Jin & Cha, Seung Bong (2018). An analysis of structural relationships among university students' intention to use MOOC, personal innovativeness, academic self-efficacy, major relevance, perceived usefulness. CNU Journal of Educational Studies, 39(3), 55-81. https://doi.org/10.18612/cnujes.2018.39.3.55
  45. Park, Sun Kyung & Kang, Yoon Ji (2021). A study on the intentions of early users of Metaverse platforms using the technology acceptance model. Journal of Digital Convergence, 19(10), 275-285. https://doi.org/10.14400/JDC.2021.19.10.275
  46. Park, Sung Youl & Nam, Min Woo (2012). An analysis of structural equation model in understating university students' behavioral intention to use mobile learning based on technology acceptance model. Journal of Korean Association for Educational Information and Media, 18(1), 51-75.
  47. Seo, Ji-Hoon, Lee, Sun-Ar, Kim, Back-Hyun, Hwang, Tae-Jun, & Choo, Kyo-Nam (2021). Curriculum recommendation system design using learners data. Proceedings of KIIT Conference, 6, 109-112.
  48. Seo, Kyung-hwa (2021). Kiosks expansion in the foodservice industry: integrated technology acceptance model including personal innovativeness, self-efficacy, enjoyment, and customer satisfaction. Korean Journal of Hospitality and Tourism, 30(7), 73-92. https://doi.org/10.24992/KJHT.2021.10.30.07.73
  49. SNU Library, Begins Personalized Book Recommendation Services. (2019, September 30). SNU NOW, Available: https://now.snu.ac.kr/past/47/2/1438
  50. Yang, Youjung, Lim, Euysoo, Park, Byungho, & Jeon, Sang-guk (2005). A study on factors influencing the intention to use of m-learning. Journal of Korean Association for Educational Information and Media, 11(1), 47-65.
  51. Yi, Jun Young & Chon, Bum Soo (2020). Determinants of user satisfaction and the intention to use OTT services. Korean Journal of Broadcasting and Telecommunication Studies, 34(4), 116-144. https://doi.org/10.22876/KAB.2020.34.4.004
  52. Yoon, Sung Uk (2004). The study on the acceptance decision factor of the mobile internet. Korean Journal of Journalism & Communication Studies, 48(3), 274-301.
  53. You, Jae Hyun & Park, Cheol (2010). A comprehensive review of technology acceptance model researches. Entrue Journal of Information Technology, 9(2), 31-50.
  54. Yu, Jongpil (2022). Concept and Understanding of Structural Equation Model by Professor Jongpil Yu (revised version). Seoul: Hannarae Academy.
  55. Agarwal, R. & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9, 204-215. https://doi.org/10.1287/isre.9.2.204
  56. Armentano, M., Christensen, I., & Schiaffino, S. (2015). Applying the technology acceptance model to evaluation of recommender systems. Polibits, 51, 73-79. https://doi.org/10.17562/PB-51-10
  57. Bandura, A. (1982). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 211.
  58. Davis, D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  59. DeLone, H. & McLean, R. (1992). Information systems success: the quest for the dependent variable. Information System Research, 3(1), 60-92. https://doi.org/10.1287/isre.3.1.60
  60. DeLone, W. H. & McLean, E. R. (2003). The Delone and Mclean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
  61. Dhelim, S., Ning, H., Aung, N., Huang, R., & Ma, J. (2020). Personality-aware product recommendation system based on user interests mining and metapath discovery. IEEE Transactions on Computational Social Systems, 8(1), 99.
  62. Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
  63. Gefen, D. & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: a study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 8.
  64. Goodhue, L. & Thompson, L. (1995). Task technology fit and individual performance. MIS Quarterly, 19(2), 213-233. https://doi.org/10.2307/249689
  65. Gu, L. & Wang, J. (2015). A task technology fit model on e-learning. Issues in Information Systems, 16(1), 69-163.
  66. IFLA (2020). IFLA Statement on Libraries and Artificial Intelligence. Available: https://repository.ifla.org/handle/123456789/1848
  67. Jin, J., Guo, H., Xu, J., Wang, X., & Wang, F. (2020). An end-to-end recommendation system for urban traffic controls and management under a parallel learning framework. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1-11.
  68. Koufaris, M. & Hampton-Sosa, W. (2004). The development of initial trust in an online company by new customers. Information & Management, 41(3), 377-397. https://doi.org/10.1016/j.im.2003.08.004
  69. Leavitt, C. & Walton, J. (1988). Development of a scale for innovativeness. Advances in Consumer Research, 2, 545.
  70. Lee, Y. & Lehto, R. (2013). User acceptance of YouTube for procedural learning: an extension of the technology acceptance model. Computers & Education, 61, 193-208. https://doi.org/10.1016/j.compedu.2012.10.001
  71. Lin, S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. International Journal of Human-Computer Studies, 70(7), 498-507. https://doi.org/10.1016/j.ijhcs.2012.01.006
  72. Petter, S., DeLone, W. H., & McLean, E. R. (2013). Information systems success: the quest for the independent variables. Journal of Management Information Systems, 29(4), 7-62. https://doi.org/10.2753/MIS0742-1222290401
  73. Rogers, E. M. (1995). Diffusion of Innovations: Modifications of a Model for Telecommunications: Springer, Berlin, Heidelberg.
  74. Schunk, D. H. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26, 207-231. https://doi.org/10.1080/00461520.1991.9653133
  75. Venkatesh, V. & Davis, D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  76. Wu, B. & Chen, X. (2017). Continuance intention to use MOOCs: integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028
  77. Yang, Y., Chen, I., Akapnar, G., Flanagan, B., & Ogata, H. (2021). Using a summarized lecture material recommendation system to enhance students' preclass preparation in a flipped classroom. Educational Technology & Society, 24(2), 1-13.