Student Challenges in Finding Extracurricular Activities: Insights From Testing an AI-Based Discovery System
ABSTRACT
Extracurricular activities support students’ growth, yet many struggle to find opportunities that match their interests and schedules. This study presents a user-centered evaluation of the ‘AI Crawler’ discovery tool concept via a survey of 34 high school students. Results show 85.3% experienced difficulties, mainly not knowing where to look (63%). Participation was diverse (Shannon Index H′ = 4.17), but students struggled to navigate available options effectively. Most supported the tool, with 79.4% finding AI helpful, 76.5% rating it beneficial, and 88.2% expecting time savings compared to current search methods. Students prioritized contact info (94.1%), descriptions (91.2%), and timing (88.2%), while noting accuracy concerns (23.5%). Freshmen (35.3%) were seen as key beneficiaries, though many believed benefits extended across all grade levels. Findings highlight significant design opportunities for AI-assisted educational tools that improve access, personalization, and equitable engagement, offering broader implications for adoption in student support systems.
INTRODUCTION.
High school students often face significant barriers in discovering extracurricular activities that match their interests, schedules, and personal responsibilities. Academic demands, part-time jobs, and family obligations can leave students less able to pursue out-of-school engagement [1-2]. These challenges are compounded by time constraints and personal obstacles that significantly impede student participation [3]. Research has shown that students perceived importance of activities significantly influences their participation decisions [4], yet many lack access to comprehensive information that would allow them to make informed choices.
Participation in extracurricular activities is robustly linked to positive developmental outcomes, including improved academic performance, greater psychological adjustment, reduced risk behaviors, and increased civic engagement [2,5-7]. After-school programs that promote social-emotional learning have significantly improved students’ behavioral adjustment and academic outcomes [8]. Moreover, the breadth and diversity of participation—such as being involved in both athletics and clubs—correlates with stronger long-term well-being [2,9-10]. Participation helps identity development, self-efficacy, and peer bonding, which contribute to students’ sense of belonging and school connectedness [11-12].
Despite these benefits, many students lack efficient ways to find and evaluate extracurricular offerings. Artificial intelligence (AI) is emerging as a potential solution that can personalize recommendations, streamline information retrieval, and increase access to reliable data [13-16]. Recent advances in AI-powered recommender systems have shown promise in educational contexts [17], offering personalized suggestions that can improve student engagement. Comprehensive surveys of AI applications in e-learning recommender systems have established the theoretical foundation for such interventions. AI-driven personalized learning systems have demonstrated significant potential in enhancing student engagement, motivation, and learning outcomes across various educational contexts [18]. Research has shown that personalized recommendations powered by AI can improve learning engagement and motivation in K-12 settings [19], while AI-enabled personalized learning paths support lifelong learning objectives [18]. Comprehensive analyses of personalization techniques in educational content recommendation systems have demonstrated their effectiveness in matching students with appropriate resources [20], providing a foundation for developing more sophisticated recommendation algorithms.
This study explores the perceived value and feasibility of an AI-based Crawler tool designed to assist high school students in locating extracurricular opportunities efficiently. It examines whether such a system can save students’ time, improve the diversity and accuracy of discovered activities, and ultimately support more informed engagement in enrichment experiences.
MATERIALS AND METHODS.
This study employs a pre-post survey design to evaluate whether an AI-based tool can assist high school students in identifying suitable extracurricular activities. The investigation is framed around five research questions (RQs) and corresponding research hypotheses (RHs), shown in Table 1. Together, these guide the empirical inquiry into the challenges students face in discovering extracurricular opportunities and the potential benefits of an AI-powered information retrieval system, hereafter referred to as the AI Crawler. The research adopts an unbalanced one-factor design that emphasizes students’ experiences and perceptions before and after exposure to the AI Crawler. This design enables the measurement of both baseline challenges (pre-survey) and post-intervention perceptions (post-survey), offering a comparative lens on the tool’s effectiveness.
| Table 1. Research questions (RQ) and hypotheses (RH) | |
| RQ | RH |
| 1. Are high school students experiencing difficulties in finding extracurricular activities? | RH1: Students experience varying levels of difficulty due to the absence of centralized, reliable, and accessible information sources. |
| 2. What types of extracurricular information can the AI Crawler retrieve? | RH2: The AI Crawler will aggregate a broad spectrum of information, including activity names, meeting times, locations, contact details, and social media links. |
| 3. Which grade level in high school would benefit most from the AI Crawler? | RH3: First-year students will benefit most, as they are new to the environment and often lack awareness of available opportunities. |
| 4. Can the AI Crawler help first-year students save time in their search for extracurricular activities? | RH4: By automating information retrieval, the AI Crawler will significantly reduce the time and effort required, lowering cognitive load and stress. |
| 5. Will the AI Crawler support students in discovering a broader range of extracurricular activities? | RH5: By sourcing information from multiple platforms – websites and social media – the AI Crawler will expand exposure to both mainstream and niche opportunities. |
Participants.
A total of 34 high school students participated in this study, representing a diverse range of backgrounds and academic interests. Participants were recruited from high schools primarily located in Las Vegas, Nevada (85.3%, n=29) and other US locations (14.7%, n=5). The sample consisted of 67.6% male (n=23) and 32.4% female (n=11) students across different grade levels: freshmen (44.1%, n=15), sophomores (32.4%, n=11), juniors (17.6%, n=6), and seniors (5.9%, n=2). The majority of participants (94.1%, n=32) were currently involved in extracurricular activities, providing relevant firsthand experience with activity discovery processes, which allowed them to offer informed perspectives on the challenges, preferences, and criteria involved in selecting and engaging with such activities.
Survey Instrument.
The survey instrument consisted of three sections designed to capture six perspectives in Current extracurricular participation patterns, Difficulties experienced in finding activities, Attitudes toward AI-assisted discovery tools, Desired features and information types, Platform preferences and concerns, and Demographic information. Questions employed a mix of Likert scales, multiple-choice options, and open-ended responses to gather both quantitative and qualitative data about student experiences and perceptions.
Data Analysis.
Quantitative data were analyzed using descriptive statistics including frequencies, percentages, and cross-tabulations. The Shannon Diversity Index was calculated to assess the diversity of extracurricular participation among students. Qualitative responses were categorized thematically to identify common concerns and suggestions.
RESULTS AND DISCUSSIONS
A total of 34 high school students participated in the study, consisting of 67.6% male (n = 23) and 32.4% female (n = 11) respondents shown in Table 2. Most participants were freshmen (44.1%) or sophomores (32.4%), while 17.6% were juniors and 5.9% were seniors. The majority of students were located in Las Vegas, Nevada (85.3%), with a smaller proportion (14.7%) from other U.S. locations. Nearly all students (94.1%) reported currently participating in extracurricular activities, while only 5.9% indicated they were not involved at the time of the survey.
| Table 2. Participant Demographics | ||
| Characteristic | n | % |
| Gender | ||
| Male | 23 | 67.6% |
| Female | 11 | 32.4% |
| Grade Level | ||
| Freshman (9th grade) | 15 | 44.1% |
| Sophomore (10th grade) | 11 | 32.4% |
| Junior (11th grade) | 6 | 17.6% |
| Senior (12th grade) | 2 | 5.9% |
| Location | ||
| Las Vegas, Nevada | 29 | 85.3% |
| Other US locations | 5 | 14.7% |
| Current Participation | ||
| Currently participating in extracurriculars | 32 | 94.1% |
| Not currently participating | 2 | 5.9% |
Student Participation in Extracurricular Activities.
As noted earlier, 94.1% of respondents (n = 32) reported that they were currently involved in one or more extracurricular activities, while only 5.9% (n = 2) indicated they were not participating in any. Among those involved, students listed 20 distinct types of activities, suggesting a high level of diversity among participation. The most frequently mentioned activity was volunteering, followed by sports, academic clubs, and arts-based programs.
Challenges in Accessing Extracurricular Opportunities.
A significant 85.3% of students reported having experienced difficulty in finding or joining extracurricular activities. Table 3 provides a detailed breakdown of the barriers students encountered when trying to access extracurricular activities. The most frequently cited barriers were not knowing where to look (63%), lack of awareness of available options (57%), and difficulty finding activities that matched personal interests (37%). Further qualitative responses revealed that students felt overwhelmed by scattered or incomplete information and expressed a desire for more centralized and personalized guidance. When asked whether they believed there was sufficient variety in the extracurricular activities their peers participated in, 73.5% of students responded “Yes,” 20.6% said “No,” and the remaining 5.9% were unsure. This suggests that while many students perceive a diverse set of opportunities, others may be limited by visibility or access to niche offerings.
| Table 3. Barriers to Extracurricular Activity Access | ||
| Barrier Type | n | % |
| Not knowing where to look | 21 | 63.0% |
| Lack of awareness of available options | 19 | 57.0% |
| Difficulty finding activities matching interests | 12 | 37.0% |
| Lack of time to research | 4 | 12.0% |
| Activities were too unorganized | 2 | 6.0% |
| Too many requirements to join | 1 | 3.0% |
| Students experiencing any difficulty | 29 | 85.3% |
| Students experiencing no difficulty | 5 | 14.7% |
Perceived Usefulness of an AI-Assisted Tool (AI Crawler).
When asked about the general idea of using AI to help find extracurricular activities, 79.4% of students (n = 27) believed it would be helpful, and 11.8% were neutral. Only three students (8.8%) indicated that it would not be helpful or expressed skepticism. When presented with the more specific concept of the AI Crawler tool, the level of support remained consistent. 76.5% of respondents affirmed that such a system would be beneficial in helping them discover extracurricular opportunities more effectively. This aligns with research demonstrating that AI-driven personalized recommendations can significantly enhance student engagement and motivation in educational contexts [21]. AI-based recommendation systems have been shown to significantly impact students’ decision-making processes, helping them make more informed choices. Tables 4 and 5 summarize student attitudes toward AI-assisted activity discovery and perceived benefits.
| Table 4. Student Attitudes Toward AI-Assisted Activity Discovery | |||||
| Attitude Measure | Very Helpful | Somewhat Helpful | Neutral | Not Very Helpful | Not Helpful |
| General AI for activity finding | 8 (23.5%) |
19 (55.9%) |
4 (11.8%) |
2 (5.9%) |
1 (2.9%) |
| Specific AI Crawler tool | 11 (32.4%) |
15 (44.1%) |
5 (14.7%) |
2 (5.9%) |
1 (2.9%) |
| Table 5. Perceived Benefits of AI Crawler | ||||
| Perceived Benefit | Yes, a lot | Yes, some | No | Total Positive |
| Time savings | 10 (29.4%) | 20 (58.8%) |
4 (11.8%) | 30 (88.2%) |
| Diverse activity discovery | 30 (88.2%) | – | 4 (11.8%) | 30 (88.2%) |
In terms of desired features, students indicated that the AI Crawler should provide activity descriptions and names, locations and meeting times, contact and joining information, social media or website links, cost-related details, and real-time updates or alerts. These preferences suggest a strong demand for a system that not only centralizes but also contextualizes and validates activity-related information. Table 6 presents the complete breakdown of information types that students want the AI Crawler to provide.
| Table 6. Desired Information Features for AI Crawler | ||
| Information Type | n | % |
| How to join/contact information | 32 | 94.1% |
| Activity name and description | 31 | 91.2% |
| Location and meeting times | 30 | 88.2% |
| Social media links | 23 | 67.6% |
| Level of popularity or student reviews | 22 | 64.7% |
| Prerequisites/requirements | 1 | 2.9% |
| Time commitment information | 1 | 2.9% |
| Difficulty level | 1 | 2.9% |
| Skills that will be gained | 1 | 2.9% |
| College/career relevance | 1 | 2.9% |
Target Audience and Time-Saving Potential.
When asked which grade level would benefit most from the AI Crawler, 50% of students believed it would help all grades equally. However, 35.3% indicated that freshmen (9th graders) would benefit the most, with smaller proportions citing juniors (8.8%), seniors (2.9%), and sophomores (2.9%). These responses align with the hypothesis that younger students, who are less familiar with the high school environment, may face more initial challenges. Table 7 shows the distribution of responses regarding which grade level would benefit most from the AI Crawler.
| Table 7. Target Audience for AI Crawler by Grade Level | ||
| Grade Level | n | % |
| All grade levels equally | 17 | 50.0% |
| 9th grade (Freshmen) | 12 | 35.3% |
| 11th grade (Juniors) | 3 | 8.8% |
| 10th grade (Sophomores) | 1 | 2.9% |
| 12th grade (Seniors) | 1 | 2.9% |
Regarding efficiency, 88.2% of students agreed that the AI Crawler would save them time compared to their current search methods. This supports the hypothesis that automating the information-gathering process would reduce both time and stress for students navigating a complex extracurricular landscape.
Potential to Increase Activity Diversity.
Most students, 88.2% (n = 30) believed the AI Crawler would help them discover a more diverse range of extracurricular activities, including less popular or harder-to-find options. This finding reinforces the tool’s potential to promote equity and inclusivity by elevating niche opportunities that may otherwise go unnoticed.
Shannon Diversity Index of Extracurricular Activities.
To assess the diversity of extracurricular involvement among students, we calculated the Shannon Diversity Index (H′) as a well-established measure for quantifying diversity in ecological and social systems. The dataset contained 70 unique activities (S = 70) and a total of 103 activity mentions after splitting multi-response entries. Example proportions include Science Olympiad (p = 4/103 ≈ 0.0388), Piano (p = 3/103 ≈ 0.0291), and DECA (p = 3/103 ≈ 0.0291). The final index was computed as: H′ = 4.17. This suggests that students are engaging in extracurriculars across academic, artistic, athletic, and social domains, creating a balanced ecosystem of opportunities.
Evenness of Extracurricular Participation.
While the Shannon Diversity Index captures overall diversity, it is also important to evaluate the evenness of participation across activities. For this, we calculated Pielou’s Evenness Index (J′), substituting the values (H′ = 4.17, S = 70) and we obtained J′ = 4.17/ln(70) ≈ 0.98. An evenness score close to 1.0 indicates that students are distributed very evenly across the range of available activities. In this dataset, no single activity disproportionately dominated student participation. This finding demonstrates that the extracurricular landscape is not only diverse in options but also inclusive and balanced in engagement, allowing students with varying interests and strengths to find relevant opportunities.
Preferred Platform and Student Feedback.
When asked about preferred platforms for deploying the AI Crawler, 50% of students indicated a preference for a traditional website, 41.2% preferred a mobile app, and the remaining 8.8% favored a web app. These findings suggest that platform flexibility will be essential for maximizing accessibility and adoption. Table 8 shows platform preferences and categorizes the main concerns students expressed about the AI Crawler.
| Table 8. Platform Preferences and Student Concerns | ||
| Platform Preference | n | % |
| Website | 17 | 50.0% |
| Mobile App | 14 | 41.2% |
| Web App | 3 | 8.8% |
| Student Concerns | n | % |
| Accuracy/False information | 8 | 23.5% |
| Privacy/Data collection | 3 | 8.8% |
| Information not up to date | 2 | 5.9% |
| AI bias/Misinformation | 2 | 5.9% |
| Competitiveness of activities | 1 | 2.9% |
| Lack of diversity in recommendations | 1 | 2.9% |
| No concerns expressed | 12 | 35.3% |
| Other/Unclear responses | 5 | 14.7% |
Students also shared concerns regarding the use of AI in this context. While some expressed worries about privacy, data accuracy, and potential bias, others raised questions about how often the information would be updated and how the tool would be maintained. These include a recommendation system based on personality or interest surveys, real-time alerts for new or upcoming activities, user reviews or peer endorsements for activities, and language support for multilingual users. Ultimately, these contributions highlight students’ eagerness not only to adopt such a tool but also to help shape its design to meet their diverse needs.
CONCLUSION AND FUTURE WORK
This study explored the challenges that high school students face in discovering extracurricular opportunities and evaluated the perceived value of an AI-based agentic tool called, The AI Crawler. The data indicates that most students have experienced difficulties in identifying
extracurricular activities, with key obstacles including not knowing where to look, limited awareness of available options, and difficulty finding activities that match their interests. These results highlight a need for tools that centralize, personalize, and simplify the information-gathering process. The AI Crawler introduced in the research was positively received: 88.2% of students believed it could help them discover a more diverse range of activities, and 88.2% believed it would save them time. Respondents emphasized the importance of including essential information such as activity names and descriptions, locations, meeting times, contact or joining details, and cost transparency. Furthermore, students also stressed the need for accuracy and reliability in the information provided, emphasizing the importance of preventing the spread of outdated or false details. Interestingly, 11.8% of students felt that the AI Crawler would not save time, suggesting that the system’s design or user experience might still present inefficiencies or misunderstandings. This perspective offers valuable direction for future research by revealing design flaws, gaps in functionality, or a mismatch between user expectations and system capabilities. The findings imply strong student support for implementing such a system across all grade levels, with particular benefit anticipated for first-year students. Table 9, in the next column, provides implications discovered from this study.
| Table 9. Summary of Key Findings | ||
| Area | Key Finding | Implication |
| Primary Obstacles | Do not know where to look | Information accessibility is the main challenge |
| AI Acceptance | Finding AI assistance helpful | Strong receptiveness to technological solutions |
| Tool Effectiveness | Believe AI Crawler would be beneficial | Specific tool concept has broad appeal |
| Time Efficiency | Expect time savings from AI Crawler | Automation addresses efficiency concerns |
| Discovery Diversity | Believe AI would increase activity diversity | Potential to promote equity and inclusion |
| Target Audience | First-year students would benefit most | Early intervention opportunities |
| Platform Preference | Prefer website over mobile/web apps | Design considerations for accessibility |
| Information Needs | Want contact/joining information | Practical information is highest priority |
| Main Concerns | Worry about information accuracy | Quality control is essential for adoption |
ACKNOWLEDGMENTS
I would like to thank all the participants for their time and valuable contributions to this study.
REFERENCES
- J. L. Mahoney, A. L. Harris, J. S. Eccles, Organized activity participation, positive youth development, and the over-scheduling hypothesis. Social Policy Report. 20, 4, 1–32 (2006).
- J. A. Fredricks, J. S. Eccles, Is extracurricular participation associated with beneficial outcomes? Concurrent and longitudinal relations. Developmental Psychology. 42, 4, 698–713 (2006).
- A. Hassan, P. Sharma, M. Johnson, Artificial intelligence in recommender systems: Recent advances and applications in education. Complex Intelligent Systems. 10, 3, 1847–1865 (2024).
- J. Kim, D. Martinez, S. Brown, Content recommendation systems in education: A comprehensive analysis of personalization techniques. Educational Technology Research Development. 72, 2, 389–412 (2024).
- N. Darling, Participation in extracurricular activities and adolescent adjustment: Cross-sectional and longitudinal findings. Journal of Youth Adolescent. 34, 5, 493–505 (2005).
- A. F. Feldman, J. L. Matjasko, The role of school-based extracurricular activities in adolescent development: A comprehensive review and future directions. Review of Educational Research. 75, 2, 159–210 (2005).
- R. W. Larson, D. M. Hansen, J. B. Dworkin, “Organized activities as contexts for development: Extracurricular activities, after-school, and community programs.” In Organized Activities as Contexts of Development, J. L. Mahoney, R. W. Larson, J. S. Eccles, Eds. (Erlbaum, 2003), pp. 1–22.
- N. R. Riggs, M. T. Greenberg, After-school youth development programs: Aspects that promote positive development. Applied Developmental Science. 8, 1, 15–26 (2004).
- J. A. Fredricks, J. S. Eccles, et al., Breadth of extracurricular participation and adolescent adjustment among African American and European American youth. Journal of Research on Adolescent. 20, 2, 307–333 (2010).
10. M. R. Linver, J. L. Roth, J. Brooks-Gunn, Patterns of adolescents’ participation in organized activities: Are sports best when combined with other activities? Development Psychology. 45, 2, 354–367 (2009). - J. S. Eccles, B. L. Barber, Student council, volunteering, basketball, or marching band: What kind of extracurricular involvement matters? Journal of Adolescent Research. 14, 1, 10–43 (1999).
- J. B. Dworkin, R. W. Larson, D. M. Hansen, Adolescents’ accounts of growth experiences in youth activities. Journal of Youth Adolescent. 32, 1, 17–26 (2003).
- R. Luckin, W. Holmes, M. Griffiths, L. B. Forcier, Intelligence Unleashed: An Argument for AI in Education. (Pearson, 2016).
- W. Holmes, M. Bialik, C. Fadel, Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. (Center for Curriculum Redesign, 2019).
- K. Holstein, S. Doroudi, Equity and artificial intelligence in education: Will “AIEd” amplify or alleviate inequities in education? arXiv preprint (2021).
- K. Porayska-Pomsta, W. Holmes, L. Nemorin, The ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education. 34, 3, 504–526 (2024).
- L. Zhang, R. Anderson, E. Davis, The impact of AI-based course-recommender system on students’ course-selection decision-making process. Applied Sciences. 14, 9, 3672 (2024).
- M. González-González, C. Martín-del-Río, F. García-Peñalvo, Crafting personalized learning paths with AI for lifelong learning: A systematic literature review. Frontiers in Education. 9, 1424386 (2024).
- A. Haikal, M. J. Khan, S. Johnson, The role of artificial intelligence in personalized learning: A case study in K-12 education. Global Education Studies Review. 9, 2, 45–62 (2024).
- M. Al-Rahman, A. Kowalski, T. Mueller, The use of AI in e-learning recommender systems: A comprehensive survey. Computer Science Review 48, 100547 (2023).
- P.-S. Tsai, M.-H. Lee, M.-C. Li, G.-J. Hwang, Effects of artificial intelligence-enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers and Education. 194, 104684 (2023)
Posted by buchanle on Tuesday, June 2, 2026 in May 2026.
Tags: AI in the loop educational technology, extracurricular engagement, student motivation, student–AI interaction