TY - GEN
T1 - Leveraging Artificial Intelligence for Enhanced Language Teaching and Learning in Higher Education
AU - Zou, Di
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/6
Y1 - 2025/6
N2 - This paper discusses the implications of artificial intelligence (AI) integration for language teaching and learning, emphasizing both associated challenges and potential opportunities. It first examines educators’ increasing concerns regarding students’ possible misuse of AI, a situation that has prompted a transition toward cognitively demanding assessment designs. These assessments require active and critical engagement with course content, promoting deeper knowledge construction rather than passive reliance on AI-generated outputs. The paper also explores the integration of AI in feedback mechanisms, demonstrating how AI-driven feedback tools can alleviate instructors’ workloads while enhancing the quality, consistency, and timeliness of feedback provided on student writing and speaking tasks. The synergistic relationship among teacher feedback, peer feedback, and AI-generated feedback is analyzed, highlighting how this combined approach accommodates diverse learner needs and contributes to improved educational outcomes. Moreover, the potential for AI to support personalized learning experiences is discussed, focusing on the creation of adaptive learning pathways tailored to individual proficiency levels. Lastly, this paper offers practical strategies and insights for educators aiming to leverage AI’s capabilities to enhance language education, ensure assessment authenticity, and support learner success amidst the evolving academic landscape.
AB - This paper discusses the implications of artificial intelligence (AI) integration for language teaching and learning, emphasizing both associated challenges and potential opportunities. It first examines educators’ increasing concerns regarding students’ possible misuse of AI, a situation that has prompted a transition toward cognitively demanding assessment designs. These assessments require active and critical engagement with course content, promoting deeper knowledge construction rather than passive reliance on AI-generated outputs. The paper also explores the integration of AI in feedback mechanisms, demonstrating how AI-driven feedback tools can alleviate instructors’ workloads while enhancing the quality, consistency, and timeliness of feedback provided on student writing and speaking tasks. The synergistic relationship among teacher feedback, peer feedback, and AI-generated feedback is analyzed, highlighting how this combined approach accommodates diverse learner needs and contributes to improved educational outcomes. Moreover, the potential for AI to support personalized learning experiences is discussed, focusing on the creation of adaptive learning pathways tailored to individual proficiency levels. Lastly, this paper offers practical strategies and insights for educators aiming to leverage AI’s capabilities to enhance language education, ensure assessment authenticity, and support learner success amidst the evolving academic landscape.
KW - Artificial Intelligence
KW - Assessment
KW - Feedback
KW - Language Education
KW - Personalized Learning
UR - https://www.scopus.com/pages/publications/105009977557
U2 - 10.1007/978-981-96-8430-4_4
DO - 10.1007/978-981-96-8430-4_4
M3 - Conference article published in proceeding or book
SN - 9789819684298
T3 - Lecture Notes in Computer Science
SP - 50
EP - 59
BT - Blended Learning. Sustainable and Flexible Smart Learning - 18th International Conference on Blended Learning, ICBL 2025, Proceedings
A2 - Ma, Will W. K.
A2 - Cheung, Simon S. K.
A2 - Li, Chen
A2 - Prayadsab, Praewpran
A2 - Mungwattana, Anan
ER -