The current survey strictly distinguishes between questionnaire respondents and interviewees, clearly defining the statistical subjects and applicable scopes for each type of data. among which the questionnaire survey mainly selected undergraduate students from Zhejiang Yuexiu University as the survey subjects. All questionnaire statistical data in the text are based on the 190 valid student questionnaires in this study, with all quantitative conclusions referring to the student population and excluding teacher sample data. To ensure sample coverage and diversity, students are covered in different grades and disciplines, including college students in science and engineering, humanities and social sciences, and arts and sports. The structured interview mainly selects professional course teachers, instructors, teaching managers, and representatives of students at different levels from Zhejiang Yuexiu University to conduct one-on-one in-depth interviews, taking into account the dual perspectives of frontline educators and learners and realizing bidirectional data complementarity between teachers and students.

This study adopts a research approach that combines empirical research and qualitative analysis, integrating quantitative data with qualitative perspectives to ensure comprehensive, objective, and truthful research results. The main research methods used are questionnaire surveys and structured interviews. At the same time, this study strictly adheres to academic research ethics standards, implements informed consent, privacy confidentiality, data storage, and ethical review requirements throughout the process, and ensures compliance, rigor, and standardization in the research process.

Firstly, questionnaire survey method. The questionnaire design mainly includes four core contents, namely basic information, the current development status of AI empowered teacher-student relationships, the existing educational difficulties in teacher-student relationships, and the demand for innovative AI education models. In addition to basic information, the other three core contents are designed around three dimensions: classroom teaching, academic research, and growth counseling. The questionnaire adopts the Likert five-level scale, with single-choice questions, multiple-choice questions, and subjective blank filling questions, and carries out quantitative research from multiple levels of use frequency, experience, problem cognition, and innovation demand. Have you ever used AI tools in learning, research, or Instructing scenarios? As the application of AI technology and the transformation mode of teacher-student relationship, whether AI has promoted the formation of a new teacher-student interaction system that integrates academic research, classroom teaching, and growth instructing at universities? What is the core dilemma of the current AI enabled three-in-one education model for teachers and students? How do you think AI technology can better empower the development of the three-in-one teacher-student relationship at universities? The above questions are the main content of this survey as shown in Table 1.

This questionnaire survey mainly relied on various forms such as Questionnaire Star, Class DingTalk Group, and offline campus distribution to distribute 200 questionnaires. After excluding invalid filling and regular answering, 190 valid questionnaires were collected, with a questionnaire collection effectiveness rate of 95%. Afterwards, the data will be organized and statistically analyzed to identify the differences in AI usage habits, teacher-student interaction experiences, and perceptions of educational difficulties among different groups.

Secondly, semi-structured interview method. In the design of interview research, differentiated interview outlines are developed for the two major groups of university teachers and students, and targeted interview questions are set around the three core elements. Targeting the teacher community, focus on interviewing them about their practical experience, work pain points, technological application challenges, and optimization needs in AI enabled classroom teaching, academic research guidance, and student growth counseling; Targeting the student community, we aim to gain a deep understanding of their real experiences, interactive shortcomings, and personalized needs in using AI tools to connect with teachers’ educational work during classroom learning, academic research, and personal growth. Semi-structured interviews can compensate for the deep-seated shortcomings of questionnaire surveys, uncover the underlying reasons that questionnaire data cannot reflect, interpret the causes behind the data, understand the true inner feelings of teachers and students, and make research conclusions more profound and realistic [7].

Table 1. Research content.

The content of the interview is mainly based on a semi-structured interview outline drafted around the core research questions, without a fixed rigid Q&A process. The core interview questions include: “The actual effectiveness and difficulties of using AI tools for academic guidance, classroom teaching, and student instructing from the perspective of teachers”, “The specific manifestations of emotional alienation and fragmented education caused by AI applications in the eyes of teachers and students”, “The actual demands of teachers and students for the integrated development of the three-in-one collaborative education”, “Improvement suggestions for the construction of AI and teacher collaborative education models and campus AI education systems”, etc.

The interview sample was selected using non-probability sampling, taking into account both sample diversity and research specificity. A total of 10 teachers and students were selected as in-depth interviewees. The interview will be conducted through a combination of offline face-to-face interviews and online voice interviews. The entire interview process will be recorded and archived in a standardized manner, including recording and writing. After the interview, transcription, viewpoint classification, and core information extraction will be completed in a timely manner. The true expression and subjective thoughts of the interviewee will be respected throughout the process, ensuring the authenticity, completeness, and effectiveness of the interview materials. The basic information of the interviewees in this interview is shown in Table 2.

Table 2. Interviewee information.

Firstly, AI technology broadens the boundaries of education and academic collaboration tends to become normalized. In terms of academic research, with the popularization of AI technology, it has gradually been applied to fields such as data processing, project assistance, literature analysis, and paper polishing, completely changing the traditional teacher-student academic communication mode and promoting the transformation of teacher-student academic collaboration from “phased guidance” to “normalized interaction”. Research data shows that 72.5% of senior students, students majoring in artificial intelligence and art at our school frequently use AI technology tools, mainly for academic viewpoint sorting, course paper writing, graduation thesis (design) creation and other scenarios. Compared to traditional academic guidance models, AI can provide students with round the clock and real-time academic assistance, solving the shortcomings of students’ accumulated academic questions and limited offline academic communication time between teachers and students in the past.

At the same time, AI effectively broadens the boundaries of academic education for teachers and students. Traditional academic interaction between teachers and students often focuses on key nodes such as graduation thesis (design) and project application, with less daily academic communication. Currently, 68.3% of students indicate that after relying on AI to complete basic academic work, their communication with teachers has shifted from “modifying basic formats and answering basic questions” to “innovating academic ideas, refining research depth, and optimizing project direction” and other high-level academic exchanges. The quality and depth of academic interaction between teachers and students have significantly improved. The interviewed professional course teachers also generally expressed that AI has shared the basic academic guidance work, allowing teachers to focus on cultivating students’ academic thinking, research ability, and innovation literacy. The professionalism and efficiency of academic collaboration between teachers and students have been greatly improved, and the teacher-student relationship in the academic education dimension has been effectively deepened.

Secondly, AI empowerment enhances quality and efficiency, making teacher-student teaching interaction more efficient. Classroom teaching is the fundamental core scenario of the three-in-one teacher-student relationship, and it is also the field where AI technology is most deeply integrated and has the most significant empowerment effect. Research data shows that 86.7% of undergraduate students in our school use AI technology tools throughout the entire process of pre-class preparation, classroom learning, and post-class review. AI has been deeply embedded in daily classroom teaching and has become an important auxiliary carrier for teacher-student teaching interaction. In terms of teaching efficiency, 78.1% of students stated that with the help of AI intelligent Q&A, knowledge point sorting, courseware analysis and other functions, they can quickly solve basic classroom questions and reduce the cost of repeated consultations; 65.3% of the surveyed teachers explicitly stated that AI has effectively undertaken mechanical and repetitive teaching tasks such as homework correction, knowledge point review, and learning situation statistics, greatly reducing the basic teaching pressure of teachers.

From the perspective of teacher-student interaction mode, traditional classrooms mainly rely on one-way teaching by teachers and passive listening by students, with a single form of interaction and limited coverage. After the intervention of AI technology tools, classroom interaction has broken the temporal and spatial limitations of education, forming a new teaching model of “AI assisted preview, precise classroom interaction, and AI post class consolidation”. In the classroom, teachers do not need to spend a lot of time answering common basic questions. They can focus on explaining key and difficult points, expanding their thinking, and providing personalized Q&A. The interaction between teachers and students in the classroom is more targeted and in-depth. The interview results also confirmed that science and engineering teachers and public course teachers have a higher recognition of AI teaching empowerment. They can rely on big data learning platforms to grasp the overall learning situation of the class, achieve hierarchical teaching and precise teaching, effectively optimize the problems of inefficient teacher-student interaction and insufficient targeted education in traditional classrooms, and significantly improve the effectiveness of teacher-student education in the classroom teaching dimension.

Firstly, the cognitive positioning of education is vague, and there is an imbalance between technology and education priorities. According to the survey questionnaire statistics and interview results, the cognitive bias of educational positioning has led to the phenomenon of technology and education putting the cart before the horse. Research data shows that 47.8% of teachers overly rely on AI to complete tasks such as project guidance, after-school Q&A, and ideological instructing, simplifying AI technology tools as a means to replace manual education; Some teachers also reject intelligent technology, adhere to traditional educational models, refuse to use AI to improve educational efficiency, and fail to clarify the correct educational positioning of “teacher centered, AI assisted, and collaborative complementary”. Some students overly rely on AI functions and regard AI as the main reliance for learning and growth, weakening their subjective learning thinking and face-to-face communication with teachers and students; 39.5% of the surveyed students prioritize seeking help from AI when encountering academic difficulties, and the frequency of actively discussing and exchanging growth issues with mentors offline has significantly decreased. These deviations deviate from the three-in-one educational construction logic that centers on teacher-student interaction and is assisted by technology.

Secondly, the three major education sectors are separated from each other, and the collaborative efforts for education are insufficient. At present, the application of AI education at universities is in a decentralized state, with academic research AI tools, classroom teaching AI systems, and growth instructing intelligent platforms operating independently without forming an integrated linkage mechanism. According to research data, 70.2% of teachers and students believe that there is a clear problem of scene fragmentation in current AI education, where teaching empowerment, academic empowerment, and Instructing empowerment are not interconnected, and there is a lack of unified educational carriers and linkage mechanisms. The learning situation data in teaching scenarios cannot empower academic guidance, academic growth cannot provide students with growth instructing, and the three dimensions of resources cannot be shared or complemented. Most students in the interview believed that academic guidance, classroom teaching, and daily growth instructing in schools are not interconnected, and teachers’ education work is independent, making it difficult to achieve comprehensive and integrated education throughout the entire process. At the same time, the problem of unclear positioning of human-computer education has become prominent, with teaching and academic scenes overly relying on AI technology, weakening the instructing role of teachers, instructing scenes completely relying on teachers, lacking technical assistance, and the lack of unified standards for human-computer collaboration in three-dimensional education. Ultimately, this has led to a lack of synergy in the three-in-one teacher-student relationship and insufficient educational synergy, making it difficult to achieve comprehensive and integrated educational goals.

Thirdly, the adaptability of AI tools is insufficient, making it difficult to implement personalized education. The three major modules of academic research, classroom teaching, and growth instructing cannot rely on AI for precise stratification and personalized education, lacking intelligent implementation carriers. In the practice of the “Academic-Teaching-Instructing” three-in-one teacher-student relationship, the shortcomings of adapting existing AI tool scenarios are prominent. According to a survey questionnaire, 71.4% of teachers stated that existing AI tools mainly focus on standardized exercise evaluation and courseware generation functions, while only 18.3% of tools can provide guidance on scientific research topics, phased academic assistance, and personalized psychological and career instructing. It is difficult for teachers to rely on AI to achieve personalized teaching, and there are still significant obstacles to targeted teaching and precise technical guidance for teachers and students; During the interview, 83.9% of the students also reported that the AI generated learning plans were heavily template based, unable to provide precise and personalized guidance based on their own research weaknesses, course learning situations, professional characteristics, learning abilities, and personality traits.

Fourthly, the offline deep interaction between teachers and students is weakened, and there is a lack of humanistic education emotions. AI can only provide knowledge-based and tool-based assistance, and cannot replace teachers’ humanistic care, value guidance, and psychological instructing. This has led to a serious lag in the dimension of growth instructing in the three-in-one education system, forming an imbalanced pattern of “emphasizing academia, emphasizing teaching, neglecting instructing, and weak humanities”, and a complete closed loop of teacher-student education has not yet been formed. From the current situation of education, growth instructing still heavily relies on offline manual work by teachers, and the integration of AI in the field of growth instructing is extremely low. AI has not yet formed an effective empowering role. Survey data shows that 62.8% of students have significantly reduced the frequency of actively communicating with teachers about learning, research, and ideological growth issues after relying on AI to solve learning and academic problems; The current teacher-student interaction is mostly limited to utilitarian scenarios such as learning and scientific research, lacking deep interaction in terms of ideological collision, emotional communication, and growth exchange. 58.5% of the surveyed teachers clearly feel that there is a lack of face-to-face emotional communication between teachers and students, and the interaction in education shows obvious instrumentalization and utilitarianism. During the interview, multiple instructors also mentioned that AI online communication has replaced a large number of offline heart-to-heart talks, and the sense of companionship, trust, and empathy between teachers and students continues to decline. AI can only solve rational academic problems and cannot replace teachers’ warm ideological guidance and humanistic care, resulting in a serious lack of educational warmth.

Fifthly, the uneven application ability of AI between teachers and students, and the lack of unified educational standards on campus. On the one hand, the problem of hierarchical differentiation in AI application abilities between teachers and students is objectively highlighted: questionnaire data shows that 76.2% of young teachers under the age of 35 are proficient in using AI to conduct learning analysis and research assisted teaching, while only 23.5% of teachers over the age of 50 can use AI education tools on a regular basis; 69.7% of the student population are proficient in science and engineering, while only 31.2% are proficient in humanities and social sciences. During the interview, young teachers and students expressed that they accept AI technology quickly and proficiently. However, middle-aged and elderly teachers and some lower grade students have weak AI operation abilities and are unable to proficiently use AI to complete collaborative education work. There is a significant gap in group application abilities [8]. On the other hand, most universities have not yet issued unified guidelines, management standards, and assessment criteria for the use of AI in campus education. The use of AI by teachers and students is unfounded and highly arbitrary, greatly reducing the actual effectiveness of AI empowerment in education. Over 83% of the interviewed teachers and students stated that there are currently no AI usage standards, tool admission criteria, and educational assessment criteria suitable for the three-in-one education model in our school. Without unified institutional constraints and overlapping capacity gaps, the application of AI by teachers and students is becoming increasingly casual. Some teachers blindly try various AI software, while students use intelligent tools without guidance, further widening the gap in the quality of childbirth among the three-in-one teachers.

Research shows that most teachers and students have cognitive biases in the use of AI, with some students overly relying on AI leading to reduced offline academic communication. At the same time, some teachers and students are unable to accurately define the educational roles of AI and teachers, resulting in a bipolar cognitive bias of “technology replacing education” or “completely rejecting technology”. Regarding the current empirical situation. University teachers and students should establish a teacher-centered, AI-assisted, human-machine collaborative, and humanistic-oriented educational philosophy [9], clarify that artificial intelligence will always serve as an auxiliary educational tool, and undertake basic work such as data statistics, resource push, and shallow Q&A; University teachers adhere to the core responsibility of cultivating virtue and nurturing talents, focusing on in-depth academic discussions, Instructing ideological values, caring for humanistic emotions, Instructing career planning, and providing psychological Instructing. They eliminate the domination of technology over the essence of education and balance the relationship between intelligent technology and humanistic education, and correct the cognitive bias in the positioning of teacher and student education from the root.

The questionnaire data shows that over 70% of teachers and students believe that there are prominent problems in the current AI education, such as the separation of academic, teaching, and tutoring scenarios, lack of resource sharing, and insufficient collaboration. In response to this practical dilemma. Universities should coordinate and integrate various intelligent systems for teaching, research, and student management, creating a comprehensive AI education platform that integrates academic research collaboration, intelligent classroom teaching, and all-round growth instructing. Connect the academic communication port between teachers and students, classroom teaching interaction port, and growth assistance warning port, realize the exchange and sharing of students’ academic data, research progress, and growth status information, promote seamless connection between academic guidance, classroom teaching, and daily Instructing, eliminate the fragmentation problem of the education sector, and unite the collaborative efforts of education.

The questionnaire results show that the homogenization problem of existing AI tools is prominent, making it difficult to adapt to students’ personalized learning, research, and growth counseling needs, and unable to achieve individualized teaching, which is the core weakness that restricts AI’s precise education. Regarding this issue, relying on the AI learning big data analysis function, we should accurately analyze the learning shortcomings, research needs, and growth difficulties of students in different majors, grades, and learning levels [10]. Teachers combine AI data analysis results to develop personalized guidance plans, differentiated classroom teaching plans, and targeted growth coaching plans, empowering AI to truly meet students’ personalized development needs, achieving precise teaching, precise coaching, and precise education, cracking the problem of mismatch between standardized intelligent education and differentiated development of students.

Regarding the research on 62.8% of students who rely on AI, there is a decrease in offline communication and emotional connection between teachers and students, as well as an imbalance in educating students due to feedback from interview counselors regarding a decrease in teacher-student companionship and empathy. Based on this empirical finding, while relying on AI to expand online communication channels between teachers and students and improve educational efficiency, we must also adhere to the core position of offline teacher-student interaction. Regularly carry out offline academic discussions, class heart to heart talks, one-on-one growth exchanges, and themed education activities, retaining the emotional companionship, ideological exchange, and spiritual resonance in traditional teacher-student interactions, forming a dual line interactive mode of “AI efficient handling of affairs + offline deep emotional education”, which not only leverages the convenience of intelligent technology, but also maintains the humanistic temperature of university teacher-student education, and resolves the practical dilemma of emotional dilution between teachers and students and the lack of educational warmth.

From the sample data, there is a significant difference in the stratification of AI application abilities between teachers and students. There is a large gap in AI usage frequency and operational ability among teachers of different age groups and students of different majors. Some teachers and students have insufficient AI application abilities and cannot effectively use AI to connect with multidimensional education work. In response to this differentiation issue, this study proposes to carry out AI teaching and lesson preparation, and conduct specialized training on AI teaching preparation, intelligent learning situation analysis, AI academic guidance, online Instructing skills, etc. for the teacher group, to help teachers proficiently use intelligent tools to simplify their education work; We offer courses for students on rational use of AI, compliant application of academic AI, and practical operation of intelligent learning tools, instructing them to use AI correctly to help their own learning and growth, narrowing the gap in AI application ability between teachers and students, and comprehensively improving the digital education and learning literacy of all teachers and students in the school.

During questionnaire surveys and interviews, teachers and students provided feedback that the school lacks AI usage norms, admission standards, and assessment criteria that are suitable for the three in one education model. The high degree of arbitrariness and lack of evidence in the use of AI by teachers and students is an important institutional cause of the chaos and uneven quality of AI education. Colleges and universities have formulated the “Campus Artificial Intelligence Education Application Management Standards” based on their own educational characteristics and practical needs, clarifying the scope, requirements, and bottom line of the use of AI tools in academic research, classroom teaching, and student Instructing. Define the boundaries of reasonable use of AI, strictly prohibit students from relying on AI to complete learning tasks such as thesis writing and course assignments, and standardize the application process of AI teaching for teachers, so that AI empowerment and education work have a system and standards to follow, and standardize the disorderly application behavior of AI among teachers and students, and comprehensively enhance the standardization and institutionalization level of the AI empowerment three in one education model.