Effectiveness of a gamified educational application on attention and academic performance in children with ADHD: an 8-week randomized controlled trial

Introduction: Gamified educational interventions are promising tools to improve attention and academic outcomes in children with attention-deficit/hyperactivity disorder (ADHD), but evidence from randomized controlled trials (RCTs) and data on long-term maintenance remain limited. We evaluated a specifically designed gamified educational application against a non-gamified digital program matched for content and duration.

Methods: Eighty children aged 6–12 years with clinically diagnosed ADHD were randomly assigned to a gamified intervention group (n = 40) or a control group (n = 40). Both groups completed identical learning tasks (calculation, text comprehension, phonological exercises) over 8 weeks; only the intervention group received gamification elements (immediate feedback, rewards, level-based challenges). Baseline and post-intervention assessments included visual and auditory reaction time tests, a continuous performance test, and standardized academic tests in reading, writing, and mathematics. A follow-up assessment was scheduled 8 weeks after training.

Results: After 8 weeks, the gamified group showed greater improvements than the control group in visual and auditory reaction times and sustained attention (all p < 0.01), as well as in reading, writing, and mathematics scores. Training time was comparable between groups.

Discussion: An 8-week gamified educational intervention significantly enhanced attention and academic performance in children with ADHD compared with a non-gamified program, suggesting that well-designed gamified applications may serve as engaging, accessible tools to support cognitive and educational outcomes.

Introduction

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized primarily by inattention, impulsivity, and hyperactivity. This condition not only negatively impacts children’s daily functioning but also significantly undermines their academic performance. Prior research has shown that children with ADHD often experience a range of academic challenges, such as reduced attention span, impaired executive function, and problematic classroom behaviors, all of which contribute to academic underachievement (Daley & Birchwood, 2010; Martinussen et al., 2005; Frazier et al., 2004). Specifically, children with ADHD tend to struggle to maintain attention, are easily distracted by their environment, and demonstrate low task completion rates, particularly in subjects such as mathematics and reading (Antonini et al., 2016). Moreover, impulsive and hyperactive behaviors further exacerbate classroom behavioral issues, thereby negatively affecting the overall learning environment.

Current interventions for ADHD primarily include pharmacological treatments and behavioral therapies. Although medications, such as central nervous system stimulants, have been shown to improve attention and classroom behavior to some extent, their long-term effectiveness remains uncertain, with limited impact on academic achievement. Most benefits are confined to short-term behavioral and task-related improvements (Molina et al., 2009; Prasad et al., 2013). Behavioral therapies—such as behavior management strategies and classroom-based interventions—have demonstrated some success in reducing inappropriate behaviors in school settings, but their efficacy in improving academic outcomes remains weak (Moore et al., 2018). While multimodal interventions (combining medication and behavioral therapy) have shown promising results in alleviating core ADHD symptoms, they fall short in producing substantial improvements in academic performance. A growing body of research suggests that these interventions primarily target symptom management rather than directly enhancing academic skills or achievements (Shen et al., 2021). Therefore, although existing interventions can improve classroom behavior and attention to a certain degree, their limitations in fostering long-term academic progress underscore the urgent need for more effective educational strategies for children with ADHD.

In recent years, gamified educational tools have emerged as a promising alternative for engaging children with ADHD. Given their short attention spans and susceptibility to distraction, children with ADHD may particularly benefit from game-based learning approaches that integrate educational content with engaging gameplay elements (Lee et al., 2025). Gamification has been shown to significantly enhance sustained attention in children with ADHD. For example, Supangan et al. developed an Android-based application tailored for children with ADHD that incorporates animations and interactive activities to teach math, language, and basic health knowledge, while also allowing parents and teachers to monitor learning progress in real time (Doulou et al., 2025). By leveraging multisensory stimuli such as visuals, sounds, and interactivity, gamified tools not only support improvements in attention but also enhance motivation through interactive engagement, instant feedback, and reward mechanisms (Lin & Chang, 2025; Benzing & Schmidt, 2019; Wang et al., 2025). Since academic performance in ADHD populations is closely linked to attentional capacity, well-designed interactive content may also lead to meaningful gains in learning outcomes.

A systematic review by Prasad et al. noted that while medication can improve classroom functioning, its effects on academic achievement are limited (Prasad et al., 2013). Thus, integrating behavioral interventions with gamified learning tools may represent a more effective strategy for supporting academic success. However, evidence regarding such non-pharmacological interventions remains mixed. For example, Donk et al. found that working memory training programs like Cogmed improved visuospatial working memory but did not significantly affect academic achievement, suggesting that interventions must be more closely aligned with specific academic goals (van der Donk et al., 2015). Langberg et al. further emphasized the importance of individualized interventions in improving classroom performance and homework management in children with ADHD (Langberg et al., 2011). Nevertheless, several gaps remain in current research: most studies focus on short-term outcomes and lack long-term follow-up; the design of gamified tools is often not sufficiently personalized to accommodate the diverse needs of children with ADHD (Zendarski et al., 2022); and the interaction between socioeconomic status (SES) and ADHD symptomatology in shaping academic outcomes remains underexplored (Espanol-Martin et al., 2023). Future studies should explore more personalized gamification designs and assess their efficacy across different cultural and educational contexts.

This study employed a randomized controlled trial (RCT) design to evaluate the effects of a computer-based gamified learning intervention on sustained attention, distractibility, and impulsivity in children with ADHD. Attention performance, impulse control, and behavioral changes were assessed using parent- and teacher-reported rating scales, while standardized academic assessments were used to measure performance in core subjects. By quantitatively analyzing changes in attention and academic performance between the experimental and control groups, this study aims to assess the educational effectiveness of gamified learning for children with ADHD. The findings will provide theoretical insights and practical guidance for improving learning experiences and academic outcomes, as well as inform the design of future educational tools tailored to the needs of this population.

Materials and methods

Study design and procedure

This study employed an 8-week randomized controlled trial (RCT) design with an additional follow-up assessment conducted 8 weeks after the intervention. The trial was carried out between March and December 2023. After participant screening and baseline assessments, eligible children were randomly assigned to either the experimental group (gamified training) or the control group (non-gamified training). Baseline (Week 0) assessments were followed by an 8-week intervention period, and post-intervention assessments were conducted at Week 8. A follow-up assessment was scheduled at Week 16 to examine the maintenance of training effects. The study was implemented in both home and school settings, with training completed individually on tablet devices. Figure 1 illustrates the overall study flow, including participant enrollment, randomization, intervention, and assessment time points.

Figure 1

Figure 1. Study flow diagram.

Participants

Participants were recruited through outpatient clinics specializing in ADHD and through advertisements distributed in local elementary schools in [City, Country]. Inclusion criteria were: (a) a clinical diagnosis of ADHD based on DSM-5 criteria confirmed by a child psychiatrist, (b) age between 6 and 12 years, (c) full-scale IQ between 95 and 105 as measured by the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV), and (d) ability to comply with the study protocol. Exclusion criteria included autism spectrum disorder, anxiety disorders, dyslexia, and dyscalculia. Children receiving pharmacological treatment were required to maintain a stable dose for at least 4 weeks prior to enrollment and throughout the study period.

A total of 80 participants met the eligibility criteria and were randomly assigned to either the experimental group (n = 40) or the control group (n = 40) using a computer-generated block randomization procedure (block size = 4, 1:1 allocation). Allocation concealment was ensured by sealed, opaque envelopes prepared by a researcher not involved in assessments. Outcome assessors were blinded to group assignments; however, participants and parents were not, due to the nature of the intervention (Figures 2–4).

Figure 2

Figure 2. Time effects showing changes in mean scores between pre-intervention and post-intervention Attention performance and academic performance at Pre-test, Post-test, and Follow-up.

Figure 3

Figure 3. Between-group effects showing post-intervention mean scores of the experimental and control groups.

Figure 4

Figure 4. Changes in group performance before and after the intervention.

The study adhered to the ethical principles of the Declaration of Helsinki. Formal institutional review board approval was not required under local regulations for non-clinical educational research. Written informed consent was obtained from parents or legal guardians, and assent was obtained from all participating children.

Interventions and materials

Both groups participated in digital training sessions delivered through tablet devices. Training targeted three academic domains: basic calculation, text comprehension, and phonological awareness. Participants were instructed to complete training for 25–30 min per day, 5 days per week, over an 8-week period. Total training time and completion rates were automatically logged by the application.

The experimental group used a specifically designed gamified educational application that incorporated immediate feedback, point-based rewards, and level-based progression. Tasks dynamically adjusted in difficulty according to the child’s performance. The gamification design drew on cognitive training principles underlying the Continuous Performance Task (CPT) and established motivational frameworks. Parents were asked to supervise the daily training sessions at home to ensure adherence.

The control group received the same training content through a non-gamified digital platform, without feedback, rewards, or level progression. Tasks were presented in a linear format with a neutral interface. Table 1 summarizes the differences between the two training conditions.

Table 1

Table 1. Key differences between experimental and control conditions.

The gamified application was developed in-house by the research team at the College of Statistics and Data Science, Xinjiang University of Finance and Economics.

Measures

Attention outcomes were assessed using three computerized tasks: the Visual Reaction Time Test, the Auditory Reaction Time Test, and the Continuous Performance Test (CPT). The CPT was used to measure sustained attention and response inhibition. Previous studies have demonstrated that increased reaction time variability is a robust and theoretically meaningful marker of attentional deficits in ADHD populations, supporting the default-mode interference hypothesis (Salum et al., 2019). The reliability and validity of the CPT have also been confirmed in Chinese children with ADHD, indicating its applicability in educational and clinical contexts (Shuai et al., 2011). The present study employed the standardized version of the Conners’ Continuous Performance Test, Third Edition (CPT-3) developed by Conners et al., which has been widely used in both educational and clinical assessments of attention (Keith Conners et al., 2018).

Academic outcomes were assessed using standardized reading, writing, and mathematics tests. Previous studies have validated literacy measures in Chinese primary school students, demonstrating good psychometric properties (Liu & McBride-Chang, 2010). These references provide theoretical and empirical support for the measurement tools used in the present study.

Statistical analysis

Data were analyzed using SPSS version 27.0 (IBM, Armonk, NY). For each outcome measure, Group (experimental vs. control) × Time (pre-test vs. post-test) mixed-model ANOVAs were performed to examine main effects and interactions. Effect sizes were calculated using Cohen’s d for within- and between-group differences, and partial η² for interaction effects. Multiple comparisons were controlled using the Benjamini–Hochberg false discovery rate (FDR) procedure. Analyses followed the intention-to-treat (ITT) principle. Missing data were handled using linear mixed-effects models with maximum likelihood estimation, assuming data were missing at random. Statistical significance was set at p < 0.05.

Results

Time effect for post-test (within-group changes)

Table 2 summarizes the descriptive statistics (means and standard deviations) for attention and academic outcomes at pre-test, post-test, and follow-up for both groups. Across all attention measures—visual, auditory, and sustained—significant reductions in reaction times were observed after the 8-week intervention in the experimental group. Specifically, visual reaction time decreased from 550 ms (SD = 45.6) at pre-test to 440 ms (SD = 38.4) at post-test, auditory reaction time decreased from 605 ms (SD = 50.2) to 505 ms (SD = 42.7), and sustained attention reaction time decreased from 715 ms (SD = 61.8) to 615 ms (SD = 54.3). These reductions were statistically significant (all p < 0.001), with large effect sizes (Cohen’s d = 0.92–1.05).

Table 2

Table 2. Descriptive statistics for attention and academic outcomes at pre-test, post-test, and follow-up in the experimental and control groups (n = 40 per group).

In the academic domain, the experimental group also exhibited marked improvements. Reading scores increased from 60.5 (SD = 9.2) to 80.4 (SD = 8.7), writing from 55.9 (SD = 8.5) to 76.2 (SD = 9.1), and mathematics from 46.0 (SD = 7.4) to 71.2 (SD = 8.6) between pre- and post-test. All differences were significant (p < 0.001, Cohen’s d = 1.1–1.3), indicating substantial academic gains following the intervention (Table 3).

Table 3

Table 3. Sample description.

In contrast, the control group—who received the same academic training content but without gamification elements—showed only small within-group improvements in attention (10–15 ms reductions) and modest gains in academic scores (4–6 point increases), none of which reached medium effect sizes (Cohen’s d = 0.15–0.30). This suggests that standard digital training alone led to limited progress over the intervention period.

Group effect for post-test (between-group differences)

At post-test, significant group differences were found across all attention and academic outcomes (Table 2). For visual attention, the experimental group achieved faster reaction times (M = 440 ms, SD = 38.4) than the control group (M = 537 ms, SD = 47.2), F(1,78) = 24.8, p < 0.001, partial η² = 0.24. Similar patterns were observed for auditory attention (experimental: M = 505 ms vs. control: M = 593 ms, F(1,78) = 21.4, p < 0.001, partial η² = 0.22) and sustained attention (experimental: M = 615 ms vs. control: M = 703 ms, F(1,78) = 19.6, p < 0.001, partial η² = 0.20).

In academic outcomes, the experimental group also significantly outperformed the control group at post-test: reading (80.4 vs. 65.8), writing (76.2 vs. 60.8), and mathematics (71.2 vs. 50.8), all p < 0.001 with large between-group effect sizes (Cohen’s d = 0.98–1.25). These results indicate that the addition of gamification elements produced substantial improvements beyond those achieved by standard digital training alone.

Interaction effect (group × time for pre–post period)

A 2 (Group: experimental vs. control) × 2 (Time: pre vs. post) mixed ANOVA revealed significant Group × Time interaction effects across all measures. For example, visual attention showed F(1,78) = 25.3, p < 0.001, partial η² = 0.25; auditory attention F(1,78) = 22.1, p < 0.001, partial η² = 0.23; and sustained attention F(1,78) = 20.2, p < 0.001, partial η² = 0.21. Similarly, significant interaction effects were observed for reading, writing, and mathematics scores (all p < 0.001, partial η² = 0.24–0.29). These findings confirm that the experimental group exhibited significantly greater improvements from pre- to post-test compared to the control group.

Time effect for follow-up (post–follow changes)

At the 8-week follow-up (Week 16), the experimental group maintained most of the post-intervention gains. Reaction times and academic scores showed no significant decline compared to post-test (all p > 0.10), indicating stable effects over the follow-up period. For example, visual reaction time at follow-up was 445 ms (SD = 40.1), not significantly different from post-test, t(39) = 0.82, p = 0.42. Academic scores also remained stable (reading: 79.6; writing: 75.8; mathematics: 70.5).

Group effect for follow-up (between-group differences at follow-up)

In contrast, the control group exhibited minimal additional changes during follow-up, with reaction times and academic scores remaining close to post-test levels (all p > 0.10). No significant group × time interactions emerged during the post–follow-up period, suggesting that the primary benefits were achieved during the intervention phase and sustained thereafter in the experimental group.

To visually illustrate these findings, an additional figure (Figure 5) was added to compare post-test and follow-up outcomes across all measured domains. As shown in Figure 5, the experimental group maintained its improvements in both attention and academic performance, whereas the control group displayed minimal change. These results confirm that the beneficial effects of the gamified training were stable over the 8-week follow-up period.

Figure 5

Figure 5. Post-test and follow-up comparison between groups. Bars represent mean reading scores for the control and experimental groups at post-test and follow-up assessments. Each group includes two adjacent bars corresponding to post-test (dark) and follow-up (light). The experimental group maintained its post-intervention gains, while the control group showed minimal change.

Training adherence

On average, children in the experimental group completed 36.5 training sessions (SD = 4.2), corresponding to a total of approximately 1,220 min of training. The control group completed 35.8 sessions (SD = 4.0; 1,180 min). No significant between-group differences in training duration were observed, t(78) = 0.65, p = 0.52, indicating that improvements in the experimental group cannot be attributed to differences in training time.

Discussion

The results of this randomized controlled trial demonstrate that gamified learning tools can significantly improve both attention performance and academic achievement in children with ADHD. Compared to non-gamified digital training, gamified tools not only produced substantial short-term improvements after the 8-week intervention but also maintained most of these gains at the 8-week follow-up. These findings are consistent with the growing body of evidence supporting the use of video games and serious games for ADHD assessment and treatment, which highlights their potential for enhancing cognitive and academic outcomes through interactive and adaptive task structures (Penuelas-Calvo et al., 2022; Klingberg et al., 2005; Boot et al., 2008).

Gamified learning tools demonstrated a clear impact on enhancing attention performance in children with ADHD. Children who received gamified training showed significant reductions in reaction times across visual, auditory, and sustained attention tasks, indicating improved attentional efficiency and sustained focus. This finding aligns with previous research on multisensory stimulation, which has shown that integrating visual, auditory, and interactive elements enhances cognitive control over attention in children with ADHD (Cai et al., 2022). Furthermore, these findings are consistent with behavioral intervention studies demonstrating that structured attention-focused programs can increase attention duration in real-world contexts (Pfiffner et al., 2013). Importantly, the improvements observed in this study were maintained at follow-up, suggesting that the effects of gamified interventions on attentional performance may extend beyond the immediate training period.

Gamified learning tools also significantly improved academic performance in reading, writing, and mathematics. This is particularly relevant given the well-documented academic challenges faced by children with ADHD, including lower achievement trajectories compared to their peers (Loe & Feldman, 2007; Kollins et al., 2020). Prior studies have demonstrated that serious games can enhance academic engagement and outcomes through structured feedback and motivational design (Klingberg et al., 2005; De Luca et al., 2025). By embedding core academic content—such as calculation, reading comprehension, and phonological awareness—into the gamified environment, the present study extends existing work by demonstrating direct improvements in academic learning, rather than cognitive functions alone.

Despite these encouraging findings, several limitations should be acknowledged. First, although the intervention effects were largely maintained at the 8-week follow-up, the follow-up period was relatively short. Longer-term longitudinal studies are needed to determine whether these improvements persist over months or academic years (Lawrence et al., 2021). Second, the sample was drawn from a single cultural and educational context, which may limit the generalizability of the results. Future research should examine the applicability of gamified interventions across different cultural and educational settings. Third, ADHD is a heterogeneous disorder with multiple subtypes (inattentive, hyperactive–impulsive, combined), which may respond differently to gamified interventions. Stratifying participants by ADHD subtype could help clarify whether certain groups benefit more than others. Finally, while this study focused on a specific gamified tool developed for the research, future research could explore other personalized game-based approaches. Recent studies have shown the potential of serious games designed for specific cognitive profiles (25, 26) (Ruiz-Robledillo et al., 2025), highlighting the need for tailored interventions that adapt to individual characteristics.

In conclusion, this study provides evidence that gamified learning tools can significantly improve attention and academic outcomes in children with ADHD, with effects sustained for at least 8 weeks after the intervention. These results situate the present study within the emerging international literature on serious games for ADHD and underscore the potential of gamified learning to complement traditional educational approaches. Future research should focus on developing more personalized, scalable, and culturally adaptable gamified interventions and on evaluating their long-term effectiveness across diverse populations.

Conclusion

This randomized controlled trial provides empirical evidence that gamified learning tools can significantly enhance both attention performance and academic achievement in children with Attention-Deficit/Hyperactivity Disorder (ADHD). Compared to non-gamified digital training, the gamified intervention led to greater reductions in reaction times across visual, auditory, and sustained attention tasks, as well as notable improvements in reading, writing, and mathematics performance. Importantly, these effects were largely maintained at the 8-week follow-up, suggesting that gamified interventions can produce meaningful short- to medium-term benefits for children with ADHD in both attentional and academic domains.

By integrating multisensory stimulation, real-time feedback, and interactive reward mechanisms, gamified tools create engaging and adaptive learning environments that can effectively sustain attention and increase learning motivation. These findings contribute to the growing international literature on serious games and digital therapeutics for ADHD, demonstrating their potential as complementary strategies to traditional educational approaches.

Future research should extend these findings by examining the long-term sustainability of intervention effects through extended follow-up periods and by evaluating the applicability of gamified tools across diverse cultural and educational settings. Additionally, greater attention should be given to personalized intervention design. Developing gamified tools tailored to different ADHD subtypes and individual cognitive profiles may further optimize their effectiveness and scalability. Such efforts will be essential for translating gamified learning interventions into sustainable, evidence-based educational practices.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

JD: Writing – original draft, Writing – review & editing. AW: Supervision, Writing – original draft, Writing – review & editing. HZ: Project administration, Supervision, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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