Lamei Wang

The rapid advancement of artificial intelligence (AI) has increasingly permeated various sectors, with generative AI technologies, such as ChatGPT and ERNIE Bot, creating unprecedented opportunities and challenges for educational practices. Dialogic learning, an enduring method of instruction, is on the brink of transformation in forms and models with the integration of these cutting-edge technologies. This paper examines existing literature on the nature of dialogic learning and the types of dialogues while highlighting the advantages and characteristics of generative AI. It explores the value implications of human-machine dialogic learning, proposes a strategic framework for crafting effective prompts to engage with generative AI, and introduces a foundational model for human-machine dialogic interaction. Furthermore, the paper outlines strategies and methodologies to promote this model, with a focus on fostering problem awareness, identifying linguistic risks, enhancing digital literacy, and mitigating the risks of technology dependency.

Powered by artificial intelligence (AI), AI Tutoring Assistant holds significant potential in simulating human tutors, instructional behaviors and functions. To explore the practical demands of AI Tutoring assistants in online learning scenarios, this study analyzed social platform user reviews and questionnaire data to evaluate their acceptance of online learning tools and influencing factors. The research identified three core learner needs: dynamic learning support, precise resource recommendations, and emotionally resonant interactive experiences. Regression analysis validated the Technology Acceptance Model (TAM), revealing that perceived usefulness and ease of use significantly influenced usage intention. This study also proposed the application of AI Tutoring Assistant, providing a new theoretical perspective for personalized learning support and empirical evidence for optimizing the user experience of artificial intelligence education products.

Generative Artificial Intelligence (AI) tools are now used by students to do their learning assessment, thus impairing the value of such learning assessment. For open education learners who aim to just obtain adult education diplomas, particularly, the cost of learning assessment is becoming increasingly low due to the use of generative AI. How to instructional design and assessment for open education learners in the era of generative AI becomes therefore an issue requiring urgent solutions. From such perspectives as the advantages and disadvantages of generative AI, the learning needs and characteristics of open education learners, and the new characteristics of learning assessment with the use of generative AI, this paper examines the dilemma of instructional design and assessment during the age of generative AI, puts forward the ideas and models for the design of homework for open education learners, and makes learning assessment design proposals with respect to renewing the methods of learning assessment, improving the AI literacy of both teachers and students, etc., with a view to offering some insight for the design of homework in the era of generative AI.

The development of intelligence technologies provides an important opportunity to promote the reform of education and teaching, and data-driven teaching has become the inevitable choice of teaching development in the new era. With teachers as the core of teaching implementation, how to realize the value of data and provide teacher support services for learners has become a research focus. Especially under the background that data acquisition and analysis become increasingly extensive, we can provide teacher support for students more accurately by using data. Starting from the current research situation, combined with students’ demand, for teacher support in the learning process, this research constructs a design model for data-driven teacher support, describes the key processes of data processing, key links in the learning process and key dimensions of teacher support, and applies it to teaching practices in the online learning environment. Combined with analysis of students’ online behaviors, academic performance analysis and questionnaire analysis, etc., the practical results show that data-driven teacher support can affect students’ academic performance and significantly promote students’ academic satisfaction.