Bronfenbrenner’s Ecological Framework explains human development as shaped by interactions across multiple environmental systems. These systems include:
Microsystem: Immediate environments (e.g., classroom); Mesosystem: Connections between microsystems; Exosystem: Indirect environments that still influence the individual; Macrosystem: Broader cultural and societal influences; and the Chronosystem: Changes over time affecting development.
The framework emphasizes proximal processes—direct interactions between individuals and their environments—as key to development.
In the context of this study on rural gifted and talented (GT) students using GenAI tutors, the framework helped analyze how these students’ experiences with GenAI (a microsystem) and their broader school environments (macrosystem) influenced their learning. Positive classroom interactions were found to motivate students, while negative experiences could lead to disengagement. Research also highlights that rural GT students may face unmet needs, potentially impacting achievement and retention.
The study utilized a hermeneutic phenomenological approach, which delves into lived experiences to understand ordinary details within a person's world and create meaning through interpretation. This method acknowledges that understanding is shaped by the researcher's background and historical context, using a "hermeneutic circle" to move back and forth between parts and the whole of an experience for deeper comprehension. Unlike other phenomenological approaches, hermeneutics does not require suspending existing knowledge, recognizing that interpretation is inherent. The researcher's biases and pre-understandings are considered part of the interpretive process, necessitating careful reflection.
The analysis method followed five steps:
1. Deciding upon a research question that allows for deep understanding. The guiding question for this study was: "How do rural secondary gifted and talented students describe their experiences utilizing intelligent tutors within a generative AI program in their classrooms?".
2. Identifying researcher's pre-understandings on the topic through continual reflection in a reflective journal.
3. Engaging in dialogue with participants through interviews to gain understanding, aiming for a "fusion of horizons" between the researcher's and participant's perspectives.
4. Gaining understanding through dialogue with collected data (transcripts, audio, field notes). This stage involved six substeps for data analysis.
5. Ensuring trustworthiness in the results and the understanding process by creating a transparent and traceable decision trail.
Researcher Role
The researcher's role was to act as a co-constructor of data with participants through dialogue. The researcher's position as an administrator in the district, having led an AI committee, determined AI use guidelines, and managed implementation and training, provided significant pre-understandings. Additionally, past experience as an elementary teacher who differentiated instruction for GT students and as a parent of a GT child offered a personal lens. These professional and personal pre-understandings were not bracketed but critically reflected upon and documented in a reflexive journal to ensure objectivity and a transparent decision-making trail.
Setting
The study was conducted in a rural secondary high school in the Southern United States. The district serves a 76% economically disadvantaged population and is a Title 1 school with 2,400 students. The high school campus has 750 students, 50 of whom are identified as GT. GT students at this site do not receive specific pull-out services; instead, their needs are met through Pre-Advanced Placement, Advanced Placement, Dual Credit, and Honors classes, though not all GT students enroll in these. All secondary teachers at the site are GT-trained.
The school implemented the "Chat for Schools" AI program by Skill Struck during the 2024-2025 school year. This program aims to enhance personalized learning and student engagement. All students and classrooms had access to the program, which supplements instruction and allows for GenAI chats.
Program Capabilities:
• For teachers: Creation of intelligent tutors based on defined parameters/personalities, ability to view student chats, misuse alerts, Canvas integration for assignments, quizzes for mastery assessment, control over student chat access, 24/7 customer support, and on-demand professional development.
• For students: Access to specialized tutors, lessons on AI use, quizzes linked to tutors, language customization, and dyslexia-friendly font options.
A trial with three teachers and 100 students occurred from January to May of the previous school year, leading to site-wide implementation in August 2024. Training involved 1.5 hours of direct instruction and content-specific stations, with ongoing support and feedback collection every six weeks through professional learning communities (PLCs).
Examples of program use:
• Biology: A tutor assisted students in differentiating vertebrates and invertebrates and steps for dissection before a physical dissection.
• Science/Lesson Creation: Students used a tutor to gain brief understanding of science concepts and then, based on their engagement, created a lesson to teach that concept to peers.
• CTE: A tutor helped students understand flight mechanics and laser wood cutting before building a wooden airplane.
Participants
The study employed a purposive sampling strategy to select participants who were identified as GT and actively using the "Chat for Schools" AI program. To be considered "active users," participants needed to have interacted with the AI program 25 or more times over the last six months of implementation.
Selection Process:
1. Students labeled as GT in the school's student information system and showing active use (25+ interactions) of the AI platform were identified.
2. Consultation with site administrators and instructors helped narrow down initial participants, considering how AI was integrated, student schedules, and likelihood of being articulate in interviews.
3. Initially, parents of 21 eligible GT students were invited to a parent night to gain consent.
4. Due to low attendance (only one parent), the method was modified: parental consent forms and student assent forms were distributed to possible participants during the school day for discussion with parents.
5. Seven participants (six females, one male; aged 15-17, M=16.57) returned signed forms and became the final participants. This sample size meets the suggested number for phenomenological research for in-depth understanding.
Privacy measures included using pseudonyms for all participants and securely storing all data (audio recordings, transcripts, notes) on an encrypted USB device and in a locked file cabinet, with identifying information anonymized. Participants were informed of their right to withdraw at any time.
Data Sources and Collection
Semi-structured face-to-face interviews were the primary data collection method, chosen to capture participants' lived experiences and facilitate a shared understanding through conversation. The interviews took place in February 2025, within one month of IRB approval, in a conference room on the participant's campus. Each interview was audio-recorded and automatically transcribed using Zoom (only audio was recorded, not video, to ease participants). Non-verbal cues were noted. While scheduled for 45 minutes, interviews ranged from 8 to 18 minutes (M = 12:26).
A semi-structured interview protocol (Appendix A) consisted of ten open-ended questions with follow-up questions as needed. The questions were developed based on the overarching research question, Bronfenbrenner's ecological framework, and the need to build rapport.
• Questions exploring the immediate environment (mesosystem): Focused on GT students' high school experience, interactions with peers and teachers.
• Questions examining direct interaction with the AI tutor (microsystem): How they used the AI tutor in the classroom, typical interactions, and differences from traditional methods.
• Questions on impact on learning (microsystem and mesosystem): How the AI tutor impacted learning, helped understand difficult concepts, and influenced motivation/interest.
• Questions on challenges and frustrations (microsystem): Difficulties encountered and unhelpful aspects of the AI tutor.
• Overall experience (microsystem and mesosystem): A broader reflection on their experience.
In addition to interviews, field notes captured details not in transcripts (demeanor, environment, tone), and a reflective journal documented the researcher's evolving perspectives and decisions throughout the study. All data was securely stored. Data collection ceased when data saturation was reached after analyzing all seven participants' interviews, as repetition in codes and themes was observed.
Methods of Analysis
Data analysis employed Gadamer's hermeneutic philosophy (2013), utilizing the hermeneutic circle to interpret data through the fusion of the researcher's pre-understandings and the text's meaning. This process involved six stages:
1. Immersion: Verbatim transcription of interviews, repeated reading, and open-minded reflection on pre-understandings. Member checking was performed by providing each participant their transcript for review, with no adjustments requested.
2. Understanding (Coding): Transcripts were coded using:
◦ In vivo coding: Utilizing participants' own words or phrases to capture their viewpoint.
◦ Evaluation coding: Assigning judgments about the GenAI program as expressed by participants.
3. Abstraction: Identifying and grouping codes into categories or constructs, then identifying sub-themes reflecting the researcher's theoretical or personal knowledge. These were linked to Bronfenbrenner's ecological systems (microsystem, mesosystem, exosystem, macrosystem).
4. Synthesis and Theme Development: Grouping sub-themes and elaborating on them to understand the whole text, an iterative process of refining themes against pre-existing understandings.
5. Illumination and Illustration of Phenomena: Deeply interpreting and clarifying the meaning of data, using rich descriptions and examples. Peer debriefing occurred at this stage, where a doctoral-level colleague reviewed coded transcripts and the reflective/analytical journal, confirming the accuracy of the analysis.
6. Integration and Critique: Bringing together all identified themes and interpretations to form a comprehensive and coherent understanding of the phenomenon.
No qualitative data analysis software was used; instead, an Excel sheet housed the data. The researcher maintained a reflective journal (analytic memos) throughout the analysis to document decisions and interpretations, ensuring a clear decision-making trail.
Trustworthiness
Four criteria were utilized to ensure trustworthiness:
1. Credibility: Ensured through ongoing self-reflection on biases, detailed descriptions of lived experiences, member checking (participants reviewing their transcripts), and reflective/coding journals.
2. Transferability: Achieved by providing rich, detailed descriptions of the research context, participants, and methods, allowing readers to assess applicability to other settings.
3. Dependability: Demonstrated through peer debriefing sessions with a knowledgeable colleague who reviewed half of the coded interviews and journals, finding no significant differences in analysis. Member checking also contributed to dependability.
4. Confirmability: Supported by strategies from credibility and dependability, along with an audit trail in the reflective journal documenting the research process and coding schema.
These measures collectively ensured the robustness, reliability, and validity of the study's conclusions
The findings were categorized using Bronfenbrenner's ecological framework, which views human development as shaped by interactions within various environmental layers: microsystem, mesosystem, exosystem, and macrosystem.
Four main themes emerged from the data analysis, describing the GT students' experiences with intelligent tutors in a GenAI program in their classrooms:
1. Instructional Ecology and Pragmatic Use
◦ Accelerated Workflow: Participants (85.71%) found GenAI tutors enhanced efficiency and productivity, helping them manage heavy workloads and complete complex tasks quickly. For instance, Vanessa noted that the AI "speed up the process" for research assignments, and Fallon described AI as "fast" like Google. Mark highlighted the convenience of using AI anytime, contrasting it with limited teacher availability.
◦ Cognitive Scaffold: All participants reported that GenAI tutors supported their learning by providing immediate, in-depth answers and simplifying complex tasks. Vanessa, Mark, and Gray specifically mentioned AI's help in math class, breaking down formulas and explaining concepts in an understandable way. Liz also found AI helpful for grasping concepts teachers didn't detail enough. Two participants (28.57%) used AI for studying, creating study guides or summaries.
◦ Policy and Program Context: This subtheme highlights how formal structures and high expectations placed on GT students, such as accelerated pacing and GPA requirements, influenced their use of AI. Students often faced time pressure and performance anxiety, driving them to use AI for rapid task completion. The school's instructional model, particularly the reliance on self-paced online classes and the absence of a dedicated GT program in high school, led students to use AI as a "surrogate instructor". Luna noted that online classes meant "no teachers... it's all learn yourself".
2. Affective and Relational Mediation
◦ Emotional Well-Being: Four participants (57.14%) described how GenAI mediated their emotions and social ties. Mark found that AI helped alleviate his social anxiety by allowing him to get work done without asking questions in class. Gray appreciated AI's support in coping with academic burnout and stress, finding it easier to understand questions and focus.
◦ Relational Shift: Five participants (71.42%) experienced a shift in their relationships, noting that AI tutors became surrogate instructors in settings with limited human guidance, such as online classes or when teachers were unavailable. Sophia relied on AI in her online economics class due to the lack of a physical teacher, finding it explained complex concepts effectively. Fallon used AI when teachers weren't attentive enough, highlighting its speed. Vanessa, however, expressed a preference for personal connection with a human professor.
3. Agency Building and Self-Regulation
◦ Prompt Engineering: Six participants (85.71%) emphasized the importance of developing prompt-engineering skills to interact with AI effectively. They learned to craft precise prompts or understand what not to do to get accurate and useful responses. Sophia, Mark, and Gray noted that broad questions yielded broad, unhelpful answers, necessitating specificity. Liz proactively asked "smart questions rather than just the answer" to learn as she used the tool.
◦ Critical Verification: Four participants (57.14%) recounted experiences where the AI was inaccurate, highlighting the necessity of critical evaluation and additional support. Sophia double-checked AI responses, acknowledging "AI isn't always correct". Vanessa noted AI's inaccuracies in history, particularly with specific dates. Fallon also experienced AI being wrong, necessitating further research. Luna described a conversation where the AI corrected itself after she pointed out its error.
◦ Balance and Time Management: Two participants (28.57%) discussed how AI helped them with time management and reducing procrastination. Fallon, a student-athlete, used AI to complete assignments quickly due to her compressed schedule, calling it a "coping mechanism". Vanessa found AI influenced her to do work immediately, as it allowed her to complete tasks faster than usual.
4. Meaning-Making and Identity Negotiation
◦ Perceptions of AI: All participants shared their perceptions of GenAI tutors, both positive and negative, often referring to it as a "tool". Vanessa saw AI as a sign of the world changing towards robotics. Mark emphasized using AI for studying, not just for answers, and expressed concern that many students misused it. Gray and Fallon agreed that AI is helpful when "used in the right way".
◦ Impact on Work Ethic: Five participants (71.42%) discussed AI's impact on their work ethic. Mark and Gray reported positive impacts, with AI making learning easier and fostering increased interest in subjects like math and chemistry. However, three participants (42.85%) felt it negatively affected their work ethic. Vanessa felt "less gifted" and "lazier" because AI provided answers instead of requiring her to put in effort. Liz also felt AI made her "more lazy" and dependent, reducing her reliance on self-problem-solving. Luna felt AI did not help her motivation, as it encouraged over-reliance.
◦ Identity and Equity: Two participants (28.57%) spoke passionately about how GenAI tutors affected their identity as GT students, questioning whether AI use "levels the playing field" between them and their peers. Vanessa questioned what "work am I putting in?" and felt her efforts were "unseen" if she used AI like other students, believing it made her lazier and hindered true learning. Liz also questioned her "level" as a gifted student if she depended on AI rather than figuring things out herself, though she acknowledged the work was still done correctly.
The study concluded that while GenAI tutors offer significant benefits in personalized learning and efficiency, a balanced approach integrating human interaction is essential. Educators should focus on AI literacy, differentiation, and student support to maximize AI's potential in education.
The study holds significant educational and scientific importance by addressing a critical gap in existing research concerning the use of generative AI (GenAI) intelligent tutors among gifted and talented (GT) students in secondary education.
Here's a breakdown of its importance:
1. Filling a Research Void on GT Students' Experiences with GenAI
◦ Despite growing interest in AI in education, there was limited empirical research specifically focused on the experiences and outcomes of secondary GT students interacting with GenAI in classroom settings. This study uniquely provides insights into these students' personal experiences in their own words, which was a previously unexplored area.
◦ The study contributes to a nuanced understanding of how GenAI can meet the unique learning needs of GT students, such as differentiated instruction, advanced problem-solving, and creative thinking.
2. Informing Educational Practice and Policy
◦ The findings are crucial for guiding the implementation of innovative and personalized learning opportunities for educators of GT students. By exploring these experiences, the study offers valuable insights into how emerging AI technology can be leveraged to enhance learning for GT students.
◦ It can inform the development of more effective strategies for integrating GenAI into educational practices, identifying key elements for successful AI implementation tailored to GT students' needs.
◦ The research can help educators, policymakers, and AI developers make more informed decisions about utilizing AI in education, enabling them to create policies and practices that maximize AI's potential while addressing challenges.
3. Addressing Unique Needs in Rural Settings
◦ The study is particularly significant because it focuses on rural secondary schools, which often face distinct challenges in providing adequate support and resources for GT learners. Historically, rural GT students are at risk of unmet academic and social-emotional needs, leading to underachievement and disengagement.
◦ By exploring how AI can foster more equitable educational opportunities, this research informs issues of diversity and inclusion, particularly for this underserved population. The insights gained can help address the specific needs of rural students and educators.
4. Contributing to the Broader Discourse on AI in Education
◦ The study sheds light on both the potential benefits and challenges of using GenAI in classroom settings, contributing to the broader conversation about AI's role in education. It elucidates how AI can enhance personalized educational experiences and address diverse learning needs across various student populations.
◦ It also highlights crucial educational considerations for AI, including the need for AI literacy, addressing student engagement and motivation, developing higher-order thinking skills, supporting teachers, and navigating ethical concerns like data privacy, bias, and over-reliance.
5. Methodological and Conceptual Contributions
◦ Using a hermeneutic phenomenological approach guided by Gadamer's philosophy and analyzed through Bronfenbrenner’s ecological framework, the study offers a deep, nuanced, and contextualized understanding of students' lived experiences. This approach allows for the interpretation of complex interactions between students and their learning environment, including the AI tutor.
◦ The framework helped decode how students' interactions with their educational environment and the GenAI tutor impacted their development and learning outcomes.
6. Paving the Way for Future Research
◦ The study reveals several promising areas for future research. These include exploring the long-term impact of AI on academic self-concept and intrinsic motivation, the role of teacher engagement in effective AI integration, assessing the ethical implications of AI use (such as bias and data privacy), evaluating the effectiveness of AI-driven differentiated instruction, and investigating the impact of GenAI on students’ social dynamics and transitions. This aligns with UNESCO’s AI Ethics Guidelines, encouraging the development of AI tools that support emotional resilience, social inclusion, and equitable access to academic support.
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