Hybrid Intelligence Feedback: A Model Proposal and Preliminary Study
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| Title: | Hybrid Intelligence Feedback: A Model Proposal and Preliminary Study |
|---|---|
| Language: | English |
| Authors: | Atakan Coskun (ORCID |
| Source: | Journal of Computer Assisted Learning. 2026 42(2). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 18 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Feedback (Response), Models, Higher Education, Programming, Undergraduate Students, Instructional Effectiveness, Synchronous Communication, Technology Uses in Education, Artificial Intelligence |
| DOI: | 10.1002/jcal.70212 |
| ISSN: | 0266-4909 1365-2729 |
| Abstract: | Background: Although existing studies suggest that AI chatbots hold potential as feedback tools in educational settings, there remains a significant gap in the literature regarding pedagogically grounded frameworks or guidelines for their effective implementation. Therefore, there is very little experimental research on how to use an AI chatbot based on a pedagogical approach. Objectives: This study proposed a conceptual framework grounded in a hybrid intelligence feedback model for the use of AI chatbots as pedagogical feedback tools and aimed to examine its applicability and effectiveness within a higher education programming course. Method: An experimental pre-test/post-test study involving 36 undergraduate students was conducted to evaluate the effectiveness of the proposed model and its underlying design principles for integrating AI chatbots as pedagogically grounded feedback tools in educational settings. Results and Conclusions: Theory-grounded AI chatbots based on the proposed hybrid intelligence feedback model and its design principles significantly improved learning more than the general-purpose AI chatbot in programming class. Theory-grounded AI chatbots can be an effective feedback tool in improving students' skills in self-evaluation, critical thinking, and regulating learning strategies. General-purpose AI chatbot feedback may cause "metacognitive laziness," harming learning performance. Implications: The article indicates the need for further research and validation to ensure effective implementation and address challenges associated with AI's potential inaccuracies. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1500543 |
| Database: | ERIC |
| Abstract: | Background: Although existing studies suggest that AI chatbots hold potential as feedback tools in educational settings, there remains a significant gap in the literature regarding pedagogically grounded frameworks or guidelines for their effective implementation. Therefore, there is very little experimental research on how to use an AI chatbot based on a pedagogical approach. Objectives: This study proposed a conceptual framework grounded in a hybrid intelligence feedback model for the use of AI chatbots as pedagogical feedback tools and aimed to examine its applicability and effectiveness within a higher education programming course. Method: An experimental pre-test/post-test study involving 36 undergraduate students was conducted to evaluate the effectiveness of the proposed model and its underlying design principles for integrating AI chatbots as pedagogically grounded feedback tools in educational settings. Results and Conclusions: Theory-grounded AI chatbots based on the proposed hybrid intelligence feedback model and its design principles significantly improved learning more than the general-purpose AI chatbot in programming class. Theory-grounded AI chatbots can be an effective feedback tool in improving students' skills in self-evaluation, critical thinking, and regulating learning strategies. General-purpose AI chatbot feedback may cause "metacognitive laziness," harming learning performance. Implications: The article indicates the need for further research and validation to ensure effective implementation and address challenges associated with AI's potential inaccuracies. |
|---|---|
| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1002/jcal.70212 |
