Electronic Journal of e-Learning

Designing a Gamified Retirement Planning Application: Evidence from a Workplace Study of Millennial Employees

2026-02-21T11:34:02+00:00

The phenomenon of a global retirement crisis has gained increasing attention in recent years. A growing number of individuals struggle to secure sufficient financial resources to sustain themselves during retirement. Financial knowledge not only increases the tendency to save for retirement, but it also affects an individual’s everyday money practices, including borrowing, saving, and investing decisions. Millennials are currently the largest cohort in the workforce and should be constantly made aware of the urgent need to save from the earliest age possible. The development and implementation of a unique gamified retirement planning application aimed at improving retirement preparedness was critically examined for its design rationale, user experiences, and observed successes and shortcomings. A randomised control experiment was conducted to observe any changes the gamified tool might have on the players’ retirement preparedness. Participants, millennial employees at a higher education institution, aged between 24 and 43 were randomly divided into one of three groups (gamification group, education group, and control group), ranging between 29 and 34 participants. The gamified application created a platform where the gamification group could experience the long-term effects of their decisions in a low-risk setting by modelling real-life financial decisions, which helped them develop a more accurate grasp of retirement planning. The education group was only exposed to infographics on retirement planning, and the control group was not exposed to any intervention. Data were analysed and tested using a nonparametric ANCOVA. The gamified application tends to be more effective at improving basic financial literacy, promoting interest, fostering awareness, and building confidence. However, a decrease was observed in respondents’ perceptions of how well they are prepared for retirement and whether they will achieve the income they need in retirement, which may reflect a “reality check” effect. The infographics appeared to be more effective at improving financial retirement literacy and resulted in a more positive perception of how well they are prepared for retirement, which may reflect overconfidence in their knowledge, leading them to believe they are better prepared than they actually are. Despite some content and technical limitations of the gamified application, it exhibits promise as a financial education tool, motivating individuals to take proactive steps toward retirement planning. Collectively, the evidence suggests that gamification enhances financial literacy and interest in retirement planning, prompts self-reflection, and serves as a “reality check” on respondents’ perceived confidence in retirement preparedness. Consequently, e-learning environments for financial education interventions should not only consider gamification for motivation, but also balance engagement, knowledge development, and behavioural awareness, ensuring a meaningful and realistic development of financial proficiency and behaviour. The limitations of the study suggest that the sample size limits statistical power and restricts the generalisability of the findings, the self-reported measures may be subject to social desirability bias or overconfidence, particularly among participants exposed to educational material, and, lastly, due to the time limit of the study, long-term knowledge retention and sustained behavioural change could not be assessed.

EsyGrade: An AI-Based Essay Assessment System for Economics Education

2026-02-11T15:44:45+00:00

This study aims to design, develop, and evaluate EsyGrade, a web-based essay grading system integrated with ChatGPT to support the assessment of conceptual understanding in economics education. This study is motivated by the prevalence of multiple-choice questions in Indonesian high schools due to efficiency considerations, while essay assessments better suited to measuring conceptual reasoning are still rarely used because of high assessment load. The study employed a simplified Research and Development (R&D) approach consisting of seven stages. Data were collected through expert validation using Aiken’s V, a user response questionnaire, and pretest–post-test, and were later analysed using N-Gain and effect size (Cohen’s d). The research findings indicate that EsyGrade has a high level of validity based on the expert evaluation and an excellent acceptance rate in the initial pilot test. In the main field trial, the results indicated a moderate improvement in conceptual understanding based on N-Gain, with a “large” effect size. These findings suggest that the overall change in learning outcomes indicates a fairly strong impact, although the degree of improvement varied among students. In particular, the system’s key value lies in its ability to generate structured, reflective feedback, so that it serves not only as a summative assessment tool but also as a means for formative one. This study contributes to the development of e-learning by demonstrating that AI-based essay grading systems can be designed to improve efficiency while supporting deeper learning through the integration of pedagogical principles and technology.

Evaluating an AI-Supported Revision Module for Project-Based Research in Teacher Education

2026-03-15T16:41:03+00:00

While Generative Artificial Intelligence (GenAI) allows new methods to support students in writing processes, its incorporation into university teaching needs well-designed pedagogy to ensure academic integrity and autonomy of learners, especially in Project-Based Research (PBR) courses in teacher education, where iterative feedback is important but restricted by the size of groups and limited resources. The current study aims to address the scalability problem in the feedback process by evaluating the AI-Supported Revision Module (AIRM). It is implemented as a scaffold designed according to a rubric and embedded into Moodle, using a local version of the LLaMA-2-7B model for generation of prompts based on instructor comments for revision in four modes: polishing, restructuring, justifying, and synthesizing. The purpose of the module is to provide targeted guidance to students rather than full-text generation and support revision while preserving authorship. A mixed-methods case study approach was used involving 158 primary education student teachers, out of which 132 submitted draft and revision versions, assessed using a six-criterion rubric. A mixed-design repeated measures ANOVA test revealed interaction effects for both Literature Review and Methodology at the level of p < .01, with effect sizes of d = 0.58 and d = 0.52. The results suggest more improvement in structure and synthesis for the AI group than the conventional group, while no significant differences were found for Data Justification and Interpretation, suggesting a boundary between procedural support and higher-order analytical reasoning. Process data analysis revealed active involvement of learners, evidenced by the fact that on average 14.2% of draft content was changed and 39.7% of AI suggestions were rejected. Qualitative data analysis revealed that learners utilized this AI-powered module mostly to increase text coherence and clarity while staying in control of making sense of the content, whereas teachers indicated a shift from superficial to more methodological feedback practices. Thus, the findings demonstrate that GenAI may be successfully implemented in the feedback process as an additional scaffold for revision while still respecting the agency of learners.