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The Influence of AI on the Traditional Research Paper: Using Normalization Process Theory as a Framework for Ensuring Human Participation

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Title: The Influence of AI on the Traditional Research Paper: Using Normalization Process Theory as a Framework for Ensuring Human Participation
Language: English
Authors: Lori B. Doyle, Jill L. Swisher
Source: Thresholds in Education. 2026 49(1):78-94.
Availability: Academy for Educational Studies. 2419 Berkeley Street, Springfield, MO 65804. Tel: 417-299-1560; e-mail: cqieeditors@gmail.com; Web site: http://academyforeducationalstudies.org
Peer Reviewed: Y
Page Count: 17
Publication Date: 2026
Document Type: Journal Articles
Reports - Descriptive
Education Level: Higher Education
Postsecondary Education
Descriptors: Influence of Technology, Technology Uses in Education, Artificial Intelligence, Research Papers (Students), College Students, Grounded Theory, Research Skills, Writing Skills, Theory Practice Relationship, Learning Objectives, Assignments, Documentation, Reflection, Collaborative Writing, Creativity, Public Speaking
ISSN: 0196-9641
2381-5485
Abstract: With the introduction of generative artificial intelligence (AI), the traditional research paper, as a standalone assessment measure, can no longer be trusted as the best or primary method for assessing whether students are meeting learning objectives. Educators are in the throes of rewriting, explaining, and upholding academic integrity policies while balancing the pros and cons associated with the realities of AI. The ease at which students can produce a generated paper on just about any topic must be considered at the onset and assignments should be developed with consideration regarding the level of AI susceptibility. Utilizing Normalization Process Theory (NPT) as an ecological process based on a grounded theory-to-practice methodology, the authors suggest alternative approaches to the research paper to ensure human participation in the learning process. The traditional research paper was deconstructed to determine skills, benefits, and goals of higher education assessment prior to reconstructing the process with the current AI reality in mind. The topic of AI permeation in education will continue to be an area for future research and the authors suggest some pathways for continuing the conversation along theoretical as well as practical avenues.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1504624
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Academy for Educational Studies. 2419 Berkeley Street, Springfield, MO 65804. Tel: 417-299-1560; e-mail: cqieeditors@gmail.com; Web site: http://academyforeducationalstudies.org
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  Data: With the introduction of generative artificial intelligence (AI), the traditional research paper, as a standalone assessment measure, can no longer be trusted as the best or primary method for assessing whether students are meeting learning objectives. Educators are in the throes of rewriting, explaining, and upholding academic integrity policies while balancing the pros and cons associated with the realities of AI. The ease at which students can produce a generated paper on just about any topic must be considered at the onset and assignments should be developed with consideration regarding the level of AI susceptibility. Utilizing Normalization Process Theory (NPT) as an ecological process based on a grounded theory-to-practice methodology, the authors suggest alternative approaches to the research paper to ensure human participation in the learning process. The traditional research paper was deconstructed to determine skills, benefits, and goals of higher education assessment prior to reconstructing the process with the current AI reality in mind. The topic of AI permeation in education will continue to be an area for future research and the authors suggest some pathways for continuing the conversation along theoretical as well as practical avenues.
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      – SubjectFull: Theory Practice Relationship
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      – SubjectFull: Learning Objectives
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      – SubjectFull: Assignments
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