Education, Science, Technology, Innovation and Life
Open Access
Sign In

Innovative Ideas and Approaches for College English Teaching in the Era of Artificial Intelligence

Download as PDF

DOI: 10.23977/jaip.2024.070303 | Downloads: 16 | Views: 265

Author(s)

Yuanzhi Liu 1, Huan Feng 1

Affiliation(s)

1 School of Western Languages and Cultures, Harbin, Heilongjiang, 150001, China

Corresponding Author

Yuanzhi Liu

ABSTRACT

College English public courses are often marginalized in university teaching: at the school management level, teachers and students often attach importance to professional courses and neglect public basic courses. The reduction of college English class hours, coupled with the relatively independent and free learning methods of college students, lack awareness of previewing and reviewing public courses that they do not value. College students from all over the country have varying levels of English proficiency. This study explored innovative ideas and paths for college English teaching in the era of artificial intelligence. The current college English public courses face challenges such as reduced class hours, low student attention, and uneven English proficiency. To address these issues, this article explored methods of using micro lessons and intelligent video technology to enhance teaching effectiveness, emphasizing the role of concise content and diverse forms of micro lessons in stimulating students' interest in learning. Furthermore, the design of a personalized teaching platform based on AI (artificial intelligence) technology was introduced in detail, providing personalized learning resource recommendations for students through user behavior analysis and recommendation algorithms. The experimental results showed that after applying the AI personalized recommendation platform, the average learning interest score of students increased to 4.2; the average learning time increased to 9.2 hours; the average comprehensive recommendation score reached 4.1. The AI personalized recommendation platform significantly improved students' learning interest and effectiveness. This article believed that the application of artificial intelligence in college English teaching can not only improve teaching efficiency, but also realize the personalized needs of students and promote the continuous improvement of educational quality.

KEYWORDS

Artificial Intelligence; College English Teaching; Micro Courses; Personalized Teaching; Recommendation Algorithm

CITE THIS PAPER

Yuanzhi Liu, Huan Feng, Innovative Ideas and Approaches for College English Teaching in the Era of Artificial Intelligence. Journal of Artificial Intelligence Practice (2024) Vol. 7: 16-23. DOI: http://dx.doi.org/10.23977/jaip.2024.070303.

REFERENCES

[1] Murray N, Liddicoat A J, Zhen G, et al. Constraints on innovation in English language teaching in hinterland regions of China[J]. Language Teaching Research, 2023, 27(5): 1246-1267.
[2] Su C, Pan K. Reform and Innovation of Higher Vocational Business English Reading Teaching Based on ESA Theory [J]. Creative Education, 2024, 15(4): 570-577.
[3] Zhang Y. The research on critical thinking teaching strategies in college English classroom[J]. Creative Education, 2022, 13(4): 1469-1485.
[4] Abusamra A. The role of community colleges in developing creativity and innovation skills of vocational students (the university college of applied science as a case study)[J]. Dirasat: Human and Social Sciences, 2022, 49(2): 583-598.
[5] Qian L. Research on college english teaching and quality evaluation based on data mining technology[J]. Journal of Applied Science and Engineering, 2022, 26(4): 547-556.
[6] Gumartifa A, Syahri I, Siroj R A, et al. Perception of Teachers Regarding Problem-Based Learning and Traditional Method in the Classroom Learning Innovation Process[J]. Indonesian Journal on Learning and Advanced Education (IJOLAE), 2023, 5(2): 151-166.
[7] Oktavia D, Mukminin A, Marzulina L, et al. Challenges and strategies used by English teachers in teaching English language skills to young learners[J]. Theory and Practice in Language Studies, 2022, 12(2): 382-387.
[8] Jie Z, Sunze Y. Investigating pedagogical challenges of mobile technology to English teaching[J]. Interactive Learning Environments, 2023, 31(5): 2767-2779.
[9] Shu J. A POA theory-based network teaching mode for English course in higher vocational college[J]. International Journal of Emerging Technologies in Learning (iJET), 2022, 17(1): 224-238.
[10] Wang F. Research on Innovative Models of College English Teaching in the Context of Big Data Vision[J]. Adult and Higher Education, 2024, 6(2): 94-100.
[11] Zhao W. Exploring college English teaching of rhetorical knowledge: A Legitimation Code Theory analysis[J]. Language Teaching Research, 2023, 27(2): 394-414.
[12] Song S. Innovative Foreign Language Education in Universities from the Perspective of Cross Cultural Communication [J]. Transactions on Comparative Education, 2023, 5(1): 63-67.
[13] Lubis M S A, Fatmawati E, Pratiwi E Y R, et al. Understanding curriculum transformation towards educational innovation in the era of all-digital technology[J]. Nazhruna: Jurnal Pendidikan Islam, 2022, 5(2): 526-542.
[14] Deng J, He J. Research on Strategies for Enhancing Teaching Ability of Teachers in Local Universities under the Background of the Internet Era[J]. The Educational Review, USA, 2023, 7(6): 833-836.
[15] Jiang L, Zang N, Zhou N, et al. English teachers' intention to use flipped teaching: Interrelationships with needs satisfaction, motivation, self-efficacy, belief, and support[J]. Computer Assisted Language Learning, 2022, 35(8): 1890-1919.
[16] Wu L, He X, Wang X, et al. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 35(5): 4425-4445.
[17] Papadakis H, Papagrigoriou A, Panagiotakis C, et al. Collaborative filtering recommender systems taxonomy[J]. Knowledge and Information Systems, 2022, 64(1): 35-74.
[18] Fkih F. Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison [J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(9): 7645-7669.
[19] Xu S, Tan J, Heinecke S, et al. Deconfounded causal collaborative filtering[J]. ACM Transactions on Recommender Systems, 2023, 1(4): 1-25. 
[20] Wu X. AHP-BP-Based Algorithms for Teaching Quality Evaluation of Flipped English Classrooms in the Context of New Media Communication. International Journal of Information Technologies and Systems Approach, 2023, 16(2): 1-12.

Downloads: 9113
Visits: 246652

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.