Randomized trial on the impact of card Game-Based teaching on learning and memory retention of neurological syndromes

Abstract

Background

Learning about neurological syndromes is an essential component of medical education, but traditional teaching methods often lack interactivity and practical engagement. This study investigates the impact of an innovative teaching approach using the Neurological Syndrome Card Game (NSCG) on students’ learning outcomes.

Methods

A randomized controlled trial design was implemented, with participants divided into an experimental group and a control group. The control group received traditional lecture-based instruction (e.g., PowerPoint presentations and case analyses), whereas the experimental group engaged in NSCG-based learning. The game involved card-matching competitions and anatomical injury location tasks to enhance students’ recognition, understanding, and memory of neurological syndromes. Learning outcomes were evaluated through assessments of learning effectiveness, knowledge retention rates, cognitive load, and learning experience.

Results

A total of 48 students participated in the study. No significant differences were found in baseline characteristics between the groups. Following the intervention, the experimental group showed significantly better immediate learning effects at 1 week (13.33 ± 2.12 vs. 11.92 ± 1.44, t = 3.344, P = 0.002), 3 weeks (12.83 ± 2.04 vs. 10.63 ± 1.86, t = 3.923, P = 0.000), and 6 weeks (10.04 ± 1.20 vs. 7.79 ± 1.61, t = 5.484, P = 0.000). In terms of long-term memory retention, the experimental group demonstrated superior knowledge retention rates at all time points: 22.53% at week 1, 29.49% at week 3, and 31.12% at week 6. Regarding cognitive load, the experimental group exhibited significantly lower total scores (46.96 ± 1.65 vs. 69.08 ± 4.06) and scores across all dimensions (P < 0.05). Evaluations of the learning experience indicated that students in the experimental group rated their interest and memory outcomes more positively.

Conclusion

The NSCG-based teaching method significantly enhances students’ learning and memory retention of neurological syndromes, reduces cognitive load, and increases learning interest. This method may be a useful approach for enhancing clinical syndrome teaching in medical education.

Background

Neurology, as a cornerstone discipline in medical education, presents significant challenges for clinical learners due to its intricate anatomical structures and complex neural functional networks [1, 2]. A primary difficulty lies in the topographic diagnosis of neurological symptoms: a single lesion site may manifest diverse clinical presentations, while distinct anatomical injuries can produce overlapping symptoms (e.g., both brainstem and cerebellar lesions may induce ataxia) [3]. This symptom-lesion “cross-overlap” phenomenon between symptoms and lesions renders traditional teacher-dominated passive learning models (lecture-based learning, LBL) ineffective in stimulating students’ deep cognitive processing, leading to low knowledge retention rates, fragmented clinical reasoning, and heightened academic anxiety and burnout [4,5,6].

Cognitive Load Theory (CLT) [6] provides a critical framework for understanding these challenges. CLT posits that working memory capacity is limited, and excessive cognitive load—categorized into intrinsic (complexity of content), extraneous (poor instructional design), and germane (effort toward schema construction)—can impede learning [6, 7]. Traditional lecture-based methods often overload learners with extraneous cognitive demands (e.g., passive note-taking, fragmented information delivery), leaving insufficient mental resources for integrating complex neurological concepts [8]. This overload may also hinder learners’ ability to make meaningful connections, which is crucial for retaining complex, interrelated knowledge [9].

To address these limitations, student-centered active learning frameworks such as problem-based learning (PBL) and team-based learning (TBL) have gained traction [10, 11]. While these methods enhance case analysis skills through clinical simulations, they remain confined to structured classroom discussions and fail to mitigate cognitive blind spots arising from self-directed post-class learning [12].

Gamification, grounded in CLT principles, offers a promising alternative by optimizing cognitive load through interactive, scaffolded tasks that reduce extraneous demands and enhance germane processing [13, 14]. Neuroscientific evidence further supports gamified learning, demonstrating that dopamine-driven reward systems activated during gameplay strengthen memory encoding and retention [15]. For instance, a randomized trial in psychiatric semeiology reported an 18.9% higher retention and 2.3-fold greater satisfaction with a gamified “hat game” compared to traditional methods [16]. Moreover, gamification has been shown to promote collaborative learning in teaching contexts, as learners are often required to work together in teams to solve complex problems in a game-based environment [17].

Building on this theoretical and empirical foundation, we introduce the Neurological Syndrome Card Game (NSCG), an innovative tool designed to align with CLT by:

Reducing Extraneous Load: Through dynamic syndrome construction, where students match symptom cards to assemble syndromes, NSCG replaces passive information absorption with active, schema-building tasks.

Enhancing Germane Load: Real-time feedback mechanisms (e.g., verbal recitation of syndromes) and contextualized interactions (e.g., rapid anatomical localization tasks) reinforce neural pathways critical for long-term retention.

Balancing Intrinsic Load: By breaking complex syndromes into modular card components, NSCG scaffolds learning into manageable segments, aligning with the brain’s capacity for incremental information processing.

As the first application of card-based gamification in neurology education, NSCG integrates CLT principles with neuroscientific insights to address the cognitive barriers inherent in mastering neurological syndromes.

Research subjects and methods

Research subjects

This study was designed as a parallel-group, randomized controlled trial with a 1:1 allocation ratio. A total of 48 fourth-year anesthesiology students from a medical university in China were recruited. All participants had completed foundational courses in neuroanatomy and pathology but had not yet received systematic instruction on neurological syndromes.

Participant flow

Figure 1 illustrates the Consolidated Standards of Reporting Trials (CONSORT) flow diagram, detailing screening, randomization, allocation, and follow-up processes. The trial adhered strictly to CONSORT guidelines to ensure transparency and reproducibility.

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Inclusion criteria

1. (1)

Voluntary participation with signed informed consent.

2. (2)

No prior clinical internship experience in neurology.

3. (3)

No significant differences in baseline knowledge test scores (p > 0.05).

Sample size calculation

Based on preliminary pilot study data, we assumed that the NSCG teaching intervention would improve memory test scores by 15%, with a standard deviation of 10%. Using a two-tailed test (α = 0.05, power = 0.80), the minimum required sample size was calculated as 46 using G*Power [18]. Considering a potential 5% dropout rate, the final sample size was set at 48 participants.

Randomization and allocation

Participants were assigned to either the experimental group (n = 24) or the control group (n = 24) using a computer-generated block randomization method. To ensure group balance, permuted block randomization with variable block sizes (4 and 6) was used, with stratification based on prior course performance (High, Medium, Low) to minimize potential confounding factors.

Participant flow details

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Enrollment: All 48 eligible students were enrolled after meeting the inclusion criteria.

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Randomization: Participants were stratified by prior academic performance (High, Medium, Low) to ensure balanced group allocation. A permuted block randomization method with variable block sizes (4 and 6) was implemented using a computerized random number generator (RNG) by an independent statistician.

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Allocation Concealment: Opaque, sequentially numbered envelopes were used to conceal the allocation sequence until intervention assignment. An independent staff member opened the envelopes post-randomization to assign participants to either the experimental group (n = 24) or the control group (n = 24), as shown in Fig. 2.

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Blinding: Outcome assessors and data analysts were blinded to group allocation. Participants and instructors were not blinded due to the nature of the interventions.

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Baseline characteristics

There were no significant differences between the experimental and control groups in terms of age (experimental: 22.21 ± 0.779 years vs. control: 22.33 ± 0.482 years, p = 0.507), gender distribution (male: female = 9:15 vs. 15:9, p = 0.830), college entrance exam scores (p = 0.298), or baseline knowledge test scores (p = 0.657) (see Table 4), confirming successful randomization and group equivalence.

Intervention and blinding

Intervention: Both groups received 16 h of instruction over four days. The control group underwent traditional lectures, while the experimental group participated in Neurological Syndrome Card Game (NSCG) sessions (40 min/session) integrated post-lecture.

The control group received traditional lecture-based instruction, whereas the experimental group underwent NSCG teaching intervention. To minimize assessment bias, the study employed assessor blinding, with all learning outcome assessments conducted by independent evaluators who were unaware of participants’ group assignments.

While participants and instructors were aware of the assigned intervention due to the nature of the teaching methods, outcome assessors and data analysts remained blinded to group allocation to ensure objective evaluation.

Although the instructional formats differed, both interventions covered the same core content related to neurological syndromes. The total instructional time and learning objectives were standardized across groups to ensure comparability, with both groups receiving equal access to supplementary materials and review sessions.

Recruitment and follow-up timeline

Recruitment: August–October 2024.

Intervention Period: August 26–30, 2024.

Assessments: Conducted at 1 week (September 6), 3 weeks (September 20), and 6 weeks (October 11) post-intervention.

Research design and implementation

This study employed a randomized controlled trial (RCT) design, comprising three phases: baseline assessment, intervention implementation, and follow-up testing.

To ensure comparability, both groups received identical core content (six neurological syndromes) with matched teaching duration (16 h total), faculty qualifications (senior medical students with standardized training), and clinical case difficulty levels.

Intervention program

Control group

The control group received traditional lecture-based teaching structured as follows:

Core content

Six neurological syndromes (Brown-Séquard, Wallenberg, etc.)

Teaching format

16-hour curriculum across four days (4 h/day)

PowerPoint presentations with imaging/textual case studies.

Standardized instructor scripts for case discussions.

Post-class practice

Each session concluded with a 40-minute written case analysis exercise, directly corresponding to the experimental group’s game-based practice in both duration and difficulty.

Clinical cases used were designed to match the challenge level of the gamified tasks (e.g., lesion localization in Millard-Gubler syndrome).

Students verified answers using identical UV-sensitive anatomical diagrams as the experimental group.

Comparability justification

Although the delivery methods differed (lecture-based vs. gamified collaborative learning), both groups were exposed to:

Identical content coverage (syndromes, symptoms, localization tasks).

Equal total instructional and practice time.

Matched case difficulty and shared assessment tools.

Supervised learning under trained personnel ensuring structured feedback.

This design ensures that differences in outcome can reasonably be attributed to the method of instruction rather than disparities in content exposure or task complexity.

In summary, although the two groups differed in instructional delivery (lecture vs. gamified), they were matched in all other critical aspects, including total learning hours, instructional supervision, content scope, practice difficulty, and assessment tools. Therefore, any observed differences in outcomes can with reasonable confidence be attributed to the instructional method rather than discrepancies in content exposure or engagement structure.

Experimental group

The experimental group received NSCG gamified learning integrated with the control group’s foundational elements, ensuring content parity while introducing collaborative mechanics:

Basic setup

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Grouping and Faculty:

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Student Grouping: Students were divided into 6 groups, each consisting of 4 members (ensuring balanced skill levels). The game process (as shown in Fig. 3) and the scoring rules (as shown in Table 1) guided their participation.

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Supervisor Configuration: Each group was supervised by a senior medical student who had undergone standardized training. This training covered rule enforcement, feedback phrasing, and conflict resolution. The supervisor’s task list is outlined in Table 2.

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Teaching Materials and Duration:

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Symptom Cards: 24 cards (6 syndromes × 4 cards per syndrome), with anatomical damage locations marked on the back for reference and verification.

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Frequency and Duration: The game consisted of 4 sessions, each lasting 40 min, held immediately after each class.

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Class Balance: During the same time frame, the control group engaged in written case analysis practice (40 min per session), with cases of matching difficulty to the experimental group’s games.

Game process and rules

Each game session followed these steps:

Card matching competition (15 min)

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Core Rules:

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Dynamic Card Passing: Each participant starts with 4 cards. In each round, one card is passed clockwise, with a 10-second limit per pass. Exceeding the time limit results in the supervisor retrieving one card.

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Matching Goal: The team must correctly match 4 cards, then shout “Complete!”

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Team Collaboration Points: The matched cards must include contributions from at least 2 members (the supervisor records the card sources).

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Progress Management: The supervisor provides real-time progress updates (e.g., “3 out of 4 cards correct”).

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Timeout Rule: If a group does not complete matching in 5 min, they receive a hint (e.g., the name of the affected nerve).

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Verification Step: After matching, the group undergoes inter-group verification, where other groups assess the correctness. Incorrect matches result in a penalty.

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If verified, the group proceeds to the anatomical localization step.

Anatomical damage localization (20 min)

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Core Tasks:

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Recite the core symptoms of the syndrome (must be complete and accurate).

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Localize the damage (must correspond to the symptoms).

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Students must quickly tap the seat cushion to confirm their answers.

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Supporting Tools:

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Fluorescent Marker for Verification: The key parts of the anatomical diagram are pre-coated with a UV-sensitive layer, revealing the standard answer areas when exposed to UV light.

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Error Case Library: Contains common clinical cases of mislocalized anatomical damage (e.g., mislocating the pontine base damage in Millard-Gubler syndrome as the midbrain tegmentum).

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Supervisor Feedback:

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If symptoms are missing: “Please add the typical manifestations of pyramidal tract involvement.”

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If localization is inaccurate: “Do you need to recheck the corticospinal tract orientation in the pontine base?”

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Error Correction Process: Students must repeat the supervisor’s feedback before making another attempt.

Instant quiz and break (5 min)

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Fatigue Management: A 1-minute break with a neuroanatomy-related animated video is included.

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Game Effectiveness Evaluation: A random group member is selected to answer the core symptoms and localization of the syndrome. The quality of their answer contributes to the group’s score. If the error rate exceeds 30%, the group must attempt matching again.

Measurement tools

Immediate learning effect

This study utilizes a standardized short-answer question bank to assess the immediate learning effect, covering two main areas: symptom recognition and clinical application. Testing is conducted after the intervention in the 1st, 3rd, and 6th weeks using short-answer questions (4 questions × 4 points, total 16 points).

Question bank structure

1. (1)

Symptom Recognition: This section evaluates the participant’s understanding of the core symptoms of common neurological syndromes. For example: Describe the typical clinical manifestations of Horner’s syndrome. List the primary features leading to upper motor neuron paralysis.

2. (2)

Clinical Application: This section integrates case discussions with diagnostic reasoning. For example: A patient presents with right-sided ptosis, miosis, and anhidrosis; use anatomical knowledge to analyze the possible lesion site. A patient suddenly develops difficulty swallowing, hoarseness, and coughing when drinking; briefly outline the possible affected nerves and syndrome.

Scoring

The assessment is conducted using a double-blind scoring method by two neurologists to ensure objectivity and consistency. Each participant’s answer is scored by two independent evaluators. If the difference in scores exceeds 10%, a third expert reviews and determines the final score. The scoring system will be based on a 0–4 scale for each question:

4 points (Completely Correct)

The answer is accurate, complete, clearly described, and conforms to the standard definitions in neurology, with a correct explanation.

3 points (Generally Correct)

The answer is mostly correct, covering core points but lacking some details, such as missing certain clinical manifestations or incomplete anatomical lesion site explanation.

2 points (Partially Correct)

The answer covers some points but has significant omissions or inaccuracies, such as describing only symptoms without explaining the anatomical lesion site.

1 point (Minimal Correct)

The answer contains few correct elements, with numerous errors or omissions, such as only listing the disease name without providing the anatomical lesion site.

0 points (Incorrect or Missing)

The answer is completely incorrect or does not provide any useful information.

To assess inter-rater reliability, the intraclass correlation coefficient (ICC) was calculated, yielding a value of 0.89 (95% CI: 0.811–0.937), indicating good consistency among raters.

Long-term memory retention

We assess the difference in long-term memory retention between the experimental and control groups by calculating the knowledge gain rate at different time points (Week 1, Week 3, and Week 6). The formula for calculating the knowledge gain rate is:

Knowledge Gain Rate = (Post-test Score - Baseline Score) / Baseline Score × 100%.

The difference in knowledge gain rates between the two groups is calculated as follows:

Difference in Knowledge Gain Rate = Experimental Group Knowledge Gain Rate - Control Group Knowledge Gain Rate.

Cognitive load

Cognitive load is assessed using the NASA-TLX scale, initially developed by Hart and Staveland (1988) [19] and adapted into Chinese by Liang Liling et al. [20]. The survey is completed within 24 h after the final lesson. The scale consists of six items: mental demand, physical demand, time demand, performance, effort, and frustration. Each item is measured on a scale divided into 20 segments, with scores ranging from 0 to 100, representing the intensity of cognitive load from low to high. The overall cognitive load score is calculated by summing the scores of all six items and computing their arithmetic mean. The total score ranges from 0 to 100, with higher scores indicating higher cognitive load. In this study, the Cronbach’s α coefficient for the scale was 0.893, and Bartlett’s test of sphericity yielded a value of 225.554 (p < 0.000), indicating good reliability and validity.

Learning experience evaluation

A modified version of the learning experience questionnaire developed by Marks et al. [21, 22] was used to evaluate students’ perceptions of the card game. The survey was completed within 24 h after the final class. The questionnaire consists of 10 items, with responses rated on a 5-point Likert scale (5 = Strongly Agree, 4 = Agree, 3 = Neutral, 2 = Disagree, 1 = Strongly Disagree).

Quality control

Standardization of teaching content

To ensure consistency between the experimental and control groups, both groups will follow the same textbook, and the same instructor will be assigned to teach both groups. This ensures that both groups receive identical teaching content and materials, eliminating any potential discrepancies in knowledge transfer. The instructor will use a standardized PowerPoint presentation, which will include both text and images, along with case discussions. This approach ensures that all students are exposed to the same six core neurological syndromes (e.g., Brown-Séquard syndrome, Wallenberg syndrome, etc.).

Additionally, the 16 symptom cards used in the experimental group will be uniformly created to ensure they cover all core symptoms, and the content of the game segments will be consistent across all groups, ensuring parity in the teaching materials and experience.

Supervisor and student training

Supervisor Training: Supervisors were senior students trained to fully understand the design and rules of the NSCG game. They were responsible for guiding participants and facilitating the game as moderators. The training focused on:

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The rules of the card-matching competition.

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Techniques for identifying anatomical injury sites.

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Methods for providing immediate feedback to students.

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Handling unforeseen situations and encouraging student participation.

Simulation exercises are also included to prepare supervisor to facilitate the game smoothly and adjust the pace as needed to enhance learning effectiveness.

Student Training: Student training ensures that all participants understand the game rules, objectives, and processes. The training focuses on:

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The steps for the card-matching competition.

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Techniques for identifying anatomical injury sites.

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Adjusting strategies based on feedback.

Simulation exercises will help students master the game mechanics and prepare them to tackle real-world challenges effectively, ensuring active participation and improved learning outcomes.

Ethical standards

This study was approved by the Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Approval No. KY2024-014) and was conducted in accordance with the principles of the Declaration of Helsinki. In addition, the study adheres to the CONSORT 2010 guidelines for reporting randomized controlled trials. All participants signed informed consent forms and were informed that they could withdraw from the study at any time without consequence.

Data were anonymized and coded, with all original data stored on an encrypted server accessible only to the research team. To protect participants’ privacy, published research results will not contain any identifiable information.

To ensure fairness and maintain participation levels across all groups, participants in the control group who did not engage in the after-class card games will have additional opportunities to participate in an equal number of activities. These compensatory activities will mirror the original experimental activities, ensuring that control group members have equal participation opportunities and minimizing any potential bias arising from non-participation.

Throughout the study, all participants were monitored for any potential adverse effects. No severe injuries or adverse effects related to the intervention were reported.

To ensure participant safety and well-being, any reports of pain or discomfort will be addressed promptly, and participants will be reminded that they can withdraw from the study at any time without penalty. Additionally, members of the control group who did not participate in the card games will have additional opportunities to engage in the card games, ensuring equal participation for all.

This approach aims to minimize any potential harm while maintaining the integrity of the study design and ensuring fairness in participation.

Data analysis

Data analysis was performed using SPSS 26.0 statistical software. The analysis was conducted based on the originally assigned groups, with 24 participants in both the experimental and control groups. Descriptive statistics, including frequency, percentage, mean, and standard deviation, were used to summarize the demographic data of the students. The skewness and kurtosis values of the data fell within the range of ± 3, indicating that the data approximately followed a normal distribution [23]. As a result, parametric statistical methods, which assume normality, were applied for subsequent analyses.

To compare differences between groups, Chi-square tests and independent samples t-tests were employed. For outcome variables measured across multiple time points, Repeated Measures ANOVA was initially considered. However, Mauchly’s test of sphericity indicated a violation of the sphericity assumption (W = 0.63, p = 0.001). Therefore, a multivariate analysis of variance (MANOVA) was used instead to ensure the robustness of the results. Group differences were evaluated based on F-values and P-values. To control for the risk of Type I error due to multiple comparisons, Bonferroni correction was applied to adjust the significance threshold accordingly.

All tests were two-sided, and a P-value of < 0.05 was considered statistically significant.

Research results

General information of students

A total of 48 students participated in this study, consisting of 24 males (50.0%) and 24 females (50.0%). The average age of the participants was 22.27 ± 0.64 years. Regarding academic indicators, the average total entrance score was 580.56 ± 29.24 points. The mean baseline score for the short-answer test was 6.88 ± 0.64 points. Details are provided in Table 3.

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Comparison of baseline data between groups

Table 4 presents a comparison of the baseline characteristics between the experimental and control groups. In terms of gender, 37.5% of participants in the experimental group were male and 62.5% were female, while in the control group, 62.5% were male and 37.5% were female. The chi-square test revealed no significant difference in gender distribution between the two groups (χ² = 4.090, P = 0.082).

Regarding age, the average age of the experimental group was 22.21 ± 0.779 years, while the control group had an average age of 22.33 ± 0.482 years. This age difference was not statistically significant (t = -0.669, P = 0.507). For entrance scores, the experimental group had an average score of 585.00 ± 29.150, compared to 576.13 ± 29.271 in the control group. This difference was also not statistically significant (t = 1.052, P = 0.298). As for baseline short-answer test scores, the experimental group scored 6.92 ± 0.72, while the control group scored 6.83 ± 0.56, with no significant difference (t = 0.447, P = 0.657).

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Immediate learning effects

Table 5 presents a comparison of theoretical test scores before and after the intervention at different time points (1 week, 3 weeks, and 6 weeks). Prior to the intervention, the scores of the experimental group (6.92 ± 0.72) and the control group (6.83 ± 0.56) were similar, with no statistically significant difference (t = 0.447, P = 0.657).

One week after the intervention, the experimental group showed a significant improvement, with scores increasing to 13.33 ± 2.12, compared to 11.92 ± 1.44 in the control group (t = 3.344, P = 0.002). At 3 weeks post-intervention, the experimental group maintained a higher score of 12.83 ± 2.04, while the control group scored 10.63 ± 1.86 (t = 3.923, P < 0.001), with the difference becoming more pronounced. At 6 weeks, the experimental group’s score decreased slightly to 10.04 ± 1.20, while the control group scored 7.79 ± 1.61 (t = 5.484, P < 0.001), yet the experimental group continued to significantly outperform the control group.

The F-test results indicated that both the time effect and the between-group effect were significant (F_time = 218.662, F_between_groups = 36.583, F_interaction = 13.456), suggesting that the intervention had a substantial and lasting impact on learning outcomes. Effect size analysis further confirmed this trend: Cohen’s d values at 1, 3, and 6 weeks were 0.78 (95% CI: 0.19, 1.37), 1.13 (95% CI: 0.52, 1.74), and 1.58 (95% CI: 0.93, 2.23), respectively, indicating a moderate to large effect of the intervention.

These findings suggest that the intervention significantly improved learning outcomes, with the experimental group maintaining a consistently higher performance than the control group over time. The specific immediate learning effects at different time points for both groups are illustrated in Fig. 4.

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Paired comparison results

The paired comparison results indicate significant differences between the factors. Specifically, Factor 1 shows significant differences with Factors 2, 3, and 4; Factor 2 shows significant differences with Factors 1, 3, and 4; Factor 3 shows significant differences with Factors 1, 2, and 4; and Factor 4 shows significant differences with Factors 1, 2, and 3. All these differences are statistically significant (p < 0.05). The detailed results are presented in Table 6.

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Long-term memory retention

Table 7 presents the knowledge retention rates of the experimental and control groups over a three-week period. In the first week, the experimental group showed a knowledge retention rate of approximately 97.42%, significantly higher than the control group’s rate of 74.89%. The difference in retention rates between the two groups was 22.53%. By the third week, the experimental group maintained a knowledge retention rate of 85.21%, while the control group’s rate was 55.72%. This represented a 29.49% higher retention rate in the experimental group compared to the control group. By the sixth week, the experimental group’s retention rate decreased to 45.16%, while the control group’s rate was 14.04%, with a 31.12% difference between the two groups. Overall, the experimental group consistently exhibited higher knowledge retention rates at all time points, indicating that the experimental intervention was more effective in promoting long-term knowledge retention.

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Cognitive load

The experimental group exhibited significantly lower cognitive load across all dimensions compared to the control group (P < 0.05). Overall, the experimental group’s NASA-TLX total score was 46.958 ± 1.6545, which was significantly lower than the control group’s score of 69.083 ± 4.0638 (t = -24.703, P = 0.000). These results suggest that the intervention in the experimental group effectively reduced the psychological, physical, and time demands during the learning process. The detailed comparison is shown in Table 8.

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Learning experience evaluation

The Likert scale results showed that the experimental group rated the statements “Helps me remember disease knowledge well” (4.5833 ± 0.50361) and “The game is interesting” (4.5000 ± 0.51075) significantly higher. Overall, the data indicates a positive response to the activity, suggesting that the intervention was both engaging and effective in enhancing knowledge retention.

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Discussion

This study systematically verified the multidimensional effects of gamified learning in neurology education by introducing the Neurological Syndrome Card Game (NSCG). The results showed that NSCG not only significantly improved students’ immediate and long-term learning outcomes but also restructured the knowledge acquisition path by optimizing cognitive load and learning experience.

While the instructional methods differed in terms of interactivity, it is important to note that both groups received identical content, time allocation, and feedback structure. Therefore, the observed differences in learning outcomes are most likely attributable to the collaborative and gamified elements themselves, rather than variations in content exposure or instructor involvement.

The role in learning and memory

In the first week post-intervention (13.333 ± 2.1196 vs. 11.917 ± 1.4421, p < 0.05), the third week (12.833 ± 2.0359 vs. 10.625 ± 1.8606, p < 0.05), and the sixth week (10.042 ± 1.1971 vs. 7.792 ± 1.6146, p < 0.05), the experimental group significantly outperformed the control group in learning scores (F_time = 218.662, F_between_groups = 36.583, F_interaction = 13.456). Furthermore, in terms of knowledge retention, the experimental group also demonstrated clear advantages. In the first week, the experimental group’s knowledge increase rate was 97.42%, significantly higher than the control group’s 74.89% (a 22.53% difference). By the third week, the knowledge increase rate of the experimental group was 85.21%, 29.49% higher than the control group’s 55.72%. Although the knowledge increase rate of the experimental group declined to 45.16% in the sixth week, it still remained higher than the control group’s 14.04% (a 31.12% difference). Paired comparison results further supported these findings.

The findings demonstrate that the Neuroscience-Based Card Game (NSCG) significantly enhances knowledge retention, with sustained effects observable across short- and medium-term intervals. This aligns with established research on gamified learning methodologies [24, 25]. The observed benefits appear to stem from increased emotional engagement, particularly through gamified elements that may stimulate the brain’s reward pathways, thereby supporting memory encoding and consolidation. Specifically, NSCG provides immediate feedback and interactive scenarios—for example, quickly showing whether answers are correct during clinical case exercises. This helps create positive learning cycles that improve knowledge retention.

From a cognitive neuroscience perspective, two plausible processes may contribute to the observed benefits. First, the card-matching task may engage both the prefrontal cortex (involved in clinical reasoning and decision-making) and the hippocampus (central to memory consolidation), thereby supporting deeper cognitive processing [26]. Second, the game’s reward system is likely to elicit motivational responses, potentially involving dopaminergic activity in the brain’s reward-related circuits, which has been associated in prior studies with increased engagement and knowledge retention [27].

In addition, it is important to acknowledge the possibility of novelty effects—where learners’ initial enthusiasm toward gamified tools may temporarily inflate performance gains. Repeated exposure may reduce this effect over time, leading to diminishing returns [28]. To address this limitation, future versions of the NSCG should consider using more varied or adaptive formats to help maintain learner interest over time.

Cognitive load improvement

The experimental group scored significantly lower than the control group in all dimensions of cognitive load (P < 0.05). Overall, the experimental group’s NASA-TLX total score was 46.958 ± 1.6545, significantly lower than the control group’s 69.083 ± 4.0638 (t = -24.703, P = 0.000), This reduction likely stems from three NSCG design features:1)Active Learning: Replacing passive note-taking with card-matching tasks reduced working memory strain.2)Immediate Feedback: Real-time corrections prevented errors from becoming ingrained.3)Emotional Engagement: Competitive elements and rewards made learning feel less effortful.

High evaluation of learning experience

Based on the learning experience survey, students in the experimental group consistently gave high ratings, especially in “learning fun” and “knowledge integration efficiency.” This indicates that NSCG not only improved learning outcomes but also enhanced the learning experience by increasing fun and interaction. 93% of students in the experimental group believed that “card matching mechanisms help associate symptoms with memory,” further verifying NSCG’s effectiveness in facilitating knowledge internalization. This result aligns with studies showing that gamified learning enhances student motivation and engagement [29, 30]. Additionally, students in the experimental group viewed the card game as a useful learning tool and believed it increased their confidence in engaging with neurology, suggesting that gamified learning not only enhances knowledge mastery but also boosts students’ confidence and interest in future learning. Nonetheless, the Hawthorne effect cannot be excluded [31]. Knowledge gains may be partly influenced by the students’ awareness of participating in an innovative and closely monitored intervention, which may have motivated them to perform better than usual.

Mechanisms behind nscg’s effectiveness

Neurological Insights: The card-matching task may engage brain regions associated with memory formation, such as the hippocampus [32], potentially supporting long-term retention. Additionally, the competitive nature of the gameplay could stimulate dopamine release [33, 34], which might reinforce learning through positive emotional experiences.

However, it is important to note that these neural mechanisms remain hypothetical within the context of this study, as no direct physiological measurements were conducted. Therefore, caution should be exercised when generalizing these cognitive and neurobiological interpretations.

Consideration of potential confounding factors

While the randomized design minimized systematic allocation bias, several confounding factors warrant discussion. First, inherent student motivation differences—though partially mitigated by blinding participants to study objectives—could persist. Gamification enthusiasts in the experimental group might have exerted greater effort due to personal interest in novel methods, whereas control group participants receiving conventional instruction might have experienced comparative demotivation, amplifying observed differences. Second, self-selection bias at the institutional level remains possible; volunteers for educational research often demonstrate higher baseline motivation than non-participants, potentially inflating overall outcomes.

Third, while cognitive load measurements showed intergroup differences, unmeasured variables like pre-existing neurology knowledge or individual learning style preferences (e.g., visual vs. kinesthetic) might have interacted with the intervention effects. Although baseline knowledge assessments showed no significant differences (p > 0.05), subtle variations in neuroanatomical spatial reasoning abilities—particularly relevant to card-matching tasks—were not formally evaluated.

Finally, social dynamics in gamified group activities could disproportionately benefit extroverted learners through peer reinforcement, while introverted students might have experienced unintended cognitive overhead from collaborative requirements. Future iterations should incorporate personality trait assessments to quantify this interaction.

Translational medical implications of teaching paradigm

This study’s practical value is reflected in three aspects: (1) Timeliness: NSCG can achieve the knowledge retention effect of six weeks of traditional teaching in just three weeks (12.833 vs. 7.79 points); (2) Universality: 93% of students in the experimental group believed the card mechanism could be applied to other complex symptom-based teaching; (3) Sustainability: Six weeks after the intervention, the experimental group’s knowledge increase rate was still 3.2 times higher than the control group’s (45.16% vs. 14.04%). Future research can further explore the following directions: developing an adaptive card system across diseases [35], integrating VR technology to enhance situational immersion [36], and establishing mapping models between game performance and clinical thinking [37]. It is noteworthy that at six weeks, the experimental group’s scores showed natural decay, suggesting the need to establish periodic reinforcement mechanisms to maintain the long-term benefits of gamified learning.

Research limitations and future directions

This study has some limitations. First, the sample size was relatively small and limited to anesthesiology students, which may affect the generalizability of the results. Therefore, future research should include a larger and more diverse group of students from different disciplines.

Second, the short intervention period made it difficult to evaluate long-term effects on clinical competence. Longer follow-up studies are needed to assess sustained learning outcomes.

Third, although efforts were made to match content and task difficulty, the difference in teaching methods—traditional lectures versus gamified learning—means it is hard to isolate the effects of specific gamification elements. To better understand these influences, future studies could use factorial or crossover designs.

Additionally, the novelty of the game might have affected student engagement (Hawthorne effect), and a small gender imbalance might have influenced the results.

Furthermore, personality traits and students’ prior attitudes toward gamification were not measured but could play a role in how students engage with the game. These factors deserve attention in future research.

The evaluation of learning experience was based only on self-reported questionnaires. While common in educational studies, this method may be affected by response bias. The lack of complementary observational or performance-based data limits the validation of subjective feedback. Future work should include multiple assessment methods for a fuller picture.

It is also worth noting that knowledge scores declined naturally from week 3 to week 6, indicating a learning decay. To address this, future implementations should consider incorporating periodic reinforcement strategies, such as spaced review sessions, booster activities, or follow-up mini-games, to help sustain the learning gains over time.

Despite these limitations, the study provides encouraging evidence supporting the educational value of NSCG. Further research should focus on improving the game design, applying neuroscience-based learning principles, and expanding to other clinical fields and learner populations.

Conclusion

As a gamified learning tool, NSCG demonstrated significant improvements in learning outcomes, memory retention, cognitive load reduction, and enhanced learning experiences in neurology education. The application of gamified learning not only facilitates students’ knowledge acquisition but also boosts their learning interest and motivation. The findings provide a cross-disciplinary design framework for medical education gamification and empirical support for improving the learning experience in challenging courses. Future studies can further explore NSCG’s application in other medical disciplines and optimize the game design to further enhance learning outcomes.

Data availability

The datasets used and/or analyzed in this study are available from the corresponding author upon reasonable request. Author’s name and contact information: Yuhuan Zhang, [email protected].

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