Produced with Scholar

Project: Educational Theory Practice Analysis

Project Overview

Project Description

Project Requirements

The peer-reviewed project will include five major sections, with relevant sub-sections to organize your work using the CGScholar structure tool.

BUT! Please don’t use these boilerplate headings. Make them specific to your chosen topic, for instance: “Introduction: Addressing the Challenge of Learner Differences”; “The Theory of Differentiated Instruction”; “Lessons from the Research: Differentiated Instruction in Practice”; “Analyzing the Future of Differentiated Instruction in the Era of Artificial Intelligence;” “Conclusions: Challenges and Prospects for Differentiated Instruction.”

Include a publishable title, an Abstract, Keywords, and Work Icon (About this Work => Info => Title/Work Icon/Abstract/Keywords).

Overall Project Wordlength – At least 3500 words (Concentration of words should be on theory/concepts and educational practice)

Part 1: Introduction/Background

Introduce your topic. Why is this topic important? What are the main dimensions of the topic? Where in the research literature and other sources do you need to go to address this topic?

Part 2: Educational Theory/Concepts

What is the educational theory that addresses your topic? Who are the main writers or advocates? Who are their critics, and what do they say?

Your work must be in the form of an exegesis of the relevant scholarly literature that addresses and cites at least 6 scholarly sources (peer-reviewed journal articles or scholarly books).

Media: Include at least 7 media elements, such as images, diagrams, infographics, tables, embedded videos, (either uploaded into CGScholar, or embedded from other sites), web links, PDFs, datasets, or other digital media. Be sure these are well integrated into your work. Explain or discuss each media item in the text of your work. If a video is more than a few minutes long, you should refer to specific points with time codes or the particular aspects of the media object that you want your readers to focus on. Caption each item sourced from the web with a link. You don’t need to include media in the references list – this should be mainly for formal publications such as peer reviewed journal articles and scholarly monographs.

Part 3 – Educational Practice Exegesis

You will present an educational practice example, or an ensemble of practices, as applied in clearly specified learning contexts. This could be a reflection practice in which you have been involved, one you have read about in the scholarly literature, or a new or unfamiliar practice which you would like to explore. While not as detailed as in the Educational Theory section of your work, this section should be supported by scholarly sources. There is not a minimum number of scholarly sources, 6 more scholarly sources in addition to those for section 2 is a reasonable target.

This section should include the following elements:

Articulate the purpose of the practice. What problem were they trying to solve, if any? What were the implementers or researchers hoping to achieve and/or learn from implementing this practice?

Provide detailed context of the educational practice applications – what, who, when, where, etc.

Describe the findings or outcomes of the implementation. What occurred? What were the impacts? What were the conclusions?

Part 4: Analysis/Discussion

Connect the practice to the theory. How does the practice that you have analyzed in this section of your work connect with the theory that you analyzed on the previous section? Does the practice fulfill the promise of the theory? What are its limitations? What are its unrealized potentials? What is your overall interpretation of your selected topic? What do the critics say about the concept and its theory, and what are the possible rebuttals of their arguments? Are its ideals and purposes hard, easy, too easy, or too hard to realize? What does the research say? What would you recommend as a way forward? What needs more thinking in theory and research of practice?

Part 5: References (as a part of and subset of the main References Section at the end of the full work)

Include citations for all media and other curated content throughout the work (below each image and video)

Include a references section of all sources and media used throughout the work, differentiated between your Learning Module-specific content and your literature review sources.

Include a References “element” or section using APA 7th edition with at least 10 scholarly sources and media sources that you have used and referred to in the text.

Be sure to follow APA guidelines, including lowercase article titles, uppercase journal titles first letter of each word), and italicized journal titles and volumes.

Icon for Adoption of Schema

Adoption of Schema

Adoption of Schema

Adoption of Schema is a research project aimed to understand the effects of artificial intelligence on the training of future teachers.

The participant questions, relates to their general use of artificial intelligence and the specific use of artificial intelligence in educational practices. This research is conducted through a qualitative method. The measurement tool of this research project is a digital interview to remove potential biases of the researcher.

Research Question 1 is: Does the Pre-Service Educator program prepare students to mitigate cognitive disequilibrium when integrating artificial intelligence into their teaching practices?

Research Question 2 is: What role does the Pre-Service Educator program have in the training quality, mentorship, or facilitation of mitigating of cognitive disequilibrium? 

As a former educator and trainer of educators. the goal of this research is evaluating if the current training process is valid for integrating artificial intelligence. If artificial intelligence is affecting how future educator are adopting learning schemas what needs changing to help them mitigate their cognitive disequilibrium.  

 

Cognitive Disequilibrium

Cognitive Disequilibrium : is a state of cognitive imbalance that occurs when new information conflicts with existing knowledge or schemas (e.g., attitudes, beliefs, or behaviors).

 

Integrating artificial intelligence (AI) in education is rapidly transforming instructional strategies, teacher roles, and student learning processes. However, this transformation also brings with it emotional and cognitive disruptions, particularly for pre-service educators learning to adopt to these changes. A recurring theme across the educational and technological research selected is cognitive disequilibrium. Cognitive Disequilibrium is a state of mental discomfort or confusion triggered when information is introduced that contradicts with their existing thought process (mental model). In summarizing the findings from the selected research, the attempt to show relationships in how pre-service teachers experience and resolve cognitive disequilibrium in the context of AI integration is the aim. The themes intersect the role of confusion, schema adaptation, resistance to integration, and institutional support. While AI often triggers cognitive disequilibrium, it also holds the potential to resolve it. Espíndola-Ulibarri et al. (2024) show how AI can diagnose confusion in real-time, allowing for immediate instructional intervention. This positions AI as a complex but potentially transformative element in teacher training. Pre-service teachers, therefore, need not only technical skills to use AI but also a meta-cognitive awareness of how AI alters learning dynamics. Al Saadi (2024) reinforces the need for targeted training programs that prepare teachers not just to use AI tools, but to interpret and act on the data these tools offer.

Confusion as a Construct

Confusion (as a construct) is a cognitive state characterized by a lack of clarity, understanding, or certainty about something, often arising when encountering information that contradicts existing knowledge or creates conflicting interpretations, leaving a person uncertain about how to try or react. Artificial Intelligence (A.I.) refers to ability of a digital computer or computer-controlled robot to do tasks commonly associated with intelligent beings. Pre-Service Educators is a student who is training to become an educator but has not yet begun teaching professionally.

A consensus among the selected research is that confusion, as a part of cognitive disequilibrium, is a productive element in learning when properly managed. D’Mello et al. (2014) and D’Mello & Graesser (2014) highlight that confusion can lead to deeper cognitive engagement when instructional interventions help learners navigate and resolve the disequilibrium. Lodge et al. (2018) extend this argument, asserting that confusion is not inherently detrimental; it becomes constructive when it prompts self-questioning, re-evaluation, and active learning. This is relevant to my inquiry of pre-service teachers who are often introduced to AI tools related to educational delivery prior to right exposure. A potential research question is if Pre-Service teachers are adequately supported, will the support the reduction in their first stage of confusion and lead to meaningful professional growth?

 

 

Pre-Service Adaptation

In the Pre-Service Educator training process, future educators can develop cognitive disequilibrium while not addressing the misconceptions of the artificial intelligence impact on their knowledge transference.

The Pre-Service Educator’s adaption of schemas may need an improved systems to analysis the data and make future decisions. The use of a potentially more effective technological tool is a potential solution. This study intends to discover a positive discourse for the use of artificial intelligence to decrease the amount of time spent in a state of cognitive disequilibrium during the Pre-Service Educator’s learning process.

  1. Assimilate – Involves using existing schemas to understand new information.
  2. Disequilibrium – is the state of cognitive imbalance that occurs when new information doesn't fit into existing schema
  3. Accommodate – Involves changing or creating new schemas to understand new information that doesn't fit into existing schemas
  4. Equilibrium – is what motivates children to continue through the stages of cognitive development

During the adoption of schema process Per-Services Educators are assessed through their  pedagogical content knowledge (PCK) which potentially can mitigate their disequalibirum. The cited reserch studies delved into different methodologies with a varying number of participants; however, their processes are guided by how people adjusted their cognitive frameworks after experiencing new concepts. This research will not test real-time schema change; however overall schema changes will be tracked. This phenomenon is fundamental to Jean Piaget’s theories of assimilation and accommodation. Dorko (2023) and Bormanaki & Khoshhal (2017) show how cognitive disequilibrium can activate schema adaptation. As learners face challenges (integration of AI) to their existing beliefs; what is their process and who’s mentoring it becomes critical. In pre-service education, AI-induced instruction has the potential to force inexperienced teachers to reconstruct their instructional schema. When supported through reflection and structured pedagogical guidance, this reconstruction becomes an opportunity for long-term professional development.

AI Effects on Pre-Service

As technology becomes more deeply embedded into all aspects of education, the indicators of how we teach and learn will be positively or negatively affected by artificial intelligence (A.I.). The future implication of AI on Pre-Service educators, is initiation of a movement to producing better educators. If the effects of cognitive disequilibrium continues to impede the attitudes, beliefs, behaviors, and ultimately the effectiveness of artificial intelligence use by future educators, something must change. The 21st century more specifically the AI revolution is showing the educational community, it must prepare future generations of educators and learners differently and outside the traditional tools.

Considerations the educational community must address :

  1. Align the training of Per-Service Educators A.I.
  2. Reposition the focus of artificial intelligence in education.
  3. Improve accessibility to artificial intelligence in educational processes and platforms.

Festinger’s (1957) theory of cognitive dissonance remains a foundational framework for understanding resistance to technological change. Harmon-Jones & Mills (1999, 2019) elaborate on how people experience discomfort when their values or beliefs conflict with new practices. O'Brien & Sohail (2020) apply this theory specifically to the reluctance to use AI-enabled tools, showing how expectation-reality gaps can hinder adoption. Using this thought process suggests that pre-service teacher programs' or institions hesitation is not necessarily a matter of teachers’ capability of adopting technology into instruction, but often how institutions believe teacher training with technology should look. Addressing the resistance will requires need-based assessments, success models, and reassurance about the continued importance of the human role in AI-supported education.

Acceptance of AI among pre-service teachers influences contextual factors such as training quality, mentorship, and earlier exposure. Zhang et al. (2023) and Sun et al. (2024) provide empirical evidence showing that institutional support, technological self-efficacy, and perceived usefulness directly correlate with AI adoption. Shulman’s (1986) concept of pedagogical content knowledge (PCK), which emphasizes fusion of technical knowledge with content delivery skills echoes these theories. Pre-service educators who receive guidance within a PCK framework are more likely to merge AI meaningfully, and not treat it as an external tool detached from pedagogy. This transformation further complicates the emotional and cognitive transitions educators must undergo. Yet it also opens opportunities for professional redefinition. With appropriate ethical training, pre-service teachers can evolve into reflective practitioners who balance AI's power without diluting pedagogical integrity.

References

D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153-170.

D’Mello, S. K., & Graesser, A. C. (2014). Confusion. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International Handbook of Emotions in Education (pp. 289-310). Routledge.

Lodge, J. M., Kennedy, G., Lockyer, L., Arguel, A., & Pachman, M. (2018, June). Understanding difficulties and resulting confusion in learning: An integrative review. In Frontiers in Education (Vol. 3, p. 49). Frontiers Media SA.

Espíndola-Ulibarri, L., Acevedo-Mosqueda, M. E., Acevedo-Mosqueda, M. A., Gómez-Coronel, S. L., & Carreño-Aguilera, R. (2024). Diagnosis of Student Confusion Through Artificial Intelligence. Fractals, 32(01), 2450010.

Al Saadi, A. (2024). Equipping School Educators for AI Integration.

Dorko, A. (2023). Generalization, assimilation, and accommodation in schema adaptation. ERIC

Bormanaki, H. B., & Khoshhal, Y. (2017). The Role of Equilibration in Piaget's Theory of Cognitive Development and Its Implication for Receptive Skills: A Theoretical Study. Journal of Language Teaching & Research, 8(5).

Festinger, L. (1957). A Theory of Cognitive Dissonance. Stanford University Press.

Harmon-Jones, E., & Mills, J. (1999). Cognitive dissonance. Progress on a pivotal theory in social psychology. Washington, DC: American Psychological Association.

O’Brien, N., & Sohail, M. (2020). Infrequent use of AI-enabled personal assistants through the lens of cognitive dissonance theory. In HCI International 2020–Late Breaking Posters: 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I 22 (pp. 342-350). Springer International Publishing

Sun, F., Tian, P., Sun, D., Fan, Y., & Yang, Y. (2024). Pre‐service teachers' inclination to integrate AI into STEM education: Analysis of influencing factors. British Journal of Educational Technology.

Shulman, L. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14.

Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49.