Table 3. Examples of the original, AI-generated, and AI-assisted abstracts from the same article.

Type	Text
Original Abstract (Hwang, Chien & Li, 2021)	Digital game-based learning (DGBL) is a frequently adopted mode for facilitating teaching and learning, where learners are immersed in game contexts to engage in gameplay and to construct meaningful knowledge. However, without guidance, students might have difficulties organizing what they have experienced in gaming contexts. Thus, in this research, a multidimensional repertory grid (MDRG) approach was proposed, and a digital game was implemented accordingly. Moreover, an experiment was executed in a geography course to examine the effectiveness of the strategy. A total of 83 high school students (15.5 years old on average) participated in the experiment. The experimental group learned with the multidimensional repertory grid-based educational game, while the control group learned with the conventional gaming mode. The analysis results revealed that the experimental group had better learning achievement as well as higher learning motivation, self-efficacy and metacognition awareness. In addition, the behavioral analysis and interview results revealed that those learning with the proposed strategy had a greater tendency to promote higher order thinking. Accordingly, the limitations and suggestions for future research are provided.
AI-Generated Chat GPT o1	This study explores the use of a multidimensional repertory grid as a graphic organizer to systematically integrate digital games into classroom instruction, with the aim of enhancing students’ learning performances and behaviors. The repertory grid framework aligns game elements, pedagogical strategies, and learning objectives, enabling teachers to develop coherent, learner-centered game-based activities. Employing a mixed-methods approach that includes classroom observations, student feedback, and performance assessments, the research examines the extent to which this visual tool supports effective instructional design. Findings show that the repertory grid can improve knowledge acquisition, foster positive learning attitudes, and sustain student motivation. Students engaged more deeply with content, demonstrated improved problem-solving skills, and exhibited greater collaboration. The organizer also guided teachers in selecting suitable games, scaffolding learning tasks, and making informed adjustments to instruction over time. These results highlight the potential of a multidimensional repertory grid to serve as a valuable resource for educators seeking to optimize the educational benefits of digital games, ultimately shaping more dynamic, impactful, and student-centered learning experiences.
AI-Generated Gemini 2.0 Pro Experimental	This study explores the efficacy of a multidimensional repertory grid (MDRG) as a graphic organizer in facilitating the implementation of digital games for enhanced student learning. MDRGs, based on Personal Construct Theory, enable the elicitation and representation of individuals’ perceptions and understandings. We argue that MDRGs can help educators navigate the complexities of game-based learning by identifying key constructs related to learning outcomes and student behaviors. This research employs a mixed-methods approach, combining quantitative analysis of student performance data with qualitative analysis of MDRG constructs generated by both teachers and students. Findings indicate that using MDRGs as a planning and reflection tool promotes more intentional and targeted game integration. Furthermore, the process facilitated a shared understanding between teachers and students about the learning objectives and expected behaviors within the game-based environment.
AI-Assisted Chat GPT o1	Digital game-based learning (DGBL) is widely adopted to facilitate teaching and learning by immersing students in game environments, allowing them to engage in gameplay and construct meaningful knowledge. However, without proper guidance, students may find it challenging to organize what they have learned in such contexts. To address this issue, this study proposes a multidimensional repertory grid (MDRG) approach and implements it within a digital game. An experiment was conducted in a geography course with 83 high school students (mean age = 15.5 years). The experimental group learned using the MDRG-based educational game, while the control group followed a conventional game-based approach. The results indicate that students in the experimental group outperformed their counterparts in learning achievement, motivation, self-efficacy and metacognitive awareness. Behavioral observations and interview data further reveal that students using the MDRG approach engaged in higher order thinking to a greater extent. Limitations and suggestions for future studies are also discussed.
AI-Assisted Gemini 2.0 Pro Experimental	Digital game-based learning (DGBL) offers an engaging approach to education, immersing learners in game contexts to foster meaningful knowledge construction. However, students may struggle to organize their experiences without adequate guidance. This research proposes a multidimensional repertory grid (MDRG) approach, integrated into a digital game, to address this challenge. An experiment was conducted with 83 high school students (average age 15.5 years) in a geography course to evaluate the effectiveness of this strategy. The experimental group used the MDRG-based educational game, while the control group engaged with a conventional gaming mode. Results showed that the experimental group achieved significantly better learning outcomes, alongside higher levels of learning motivation, self-efficacy, and metacognitive awareness. Furthermore, behavioral analysis and interviews revealed that students using the MDRG strategy exhibited a greater tendency towards higher-order thinking. The article concludes with limitations and suggestions for future research in this area.