The Emotional Impact of Educational Productivity Videos on YouTube: A Global, Cross-Sectional Survey

This study investigates whether productivity-focused educational videos on YouTube elicit positive (e.g., motivation, inspiration) or negative (e.g., anxiety, inadequacy) emotions among viewers. Social media has enabled rapid dissemination of educational content, and YouTube is widely used by students to supplement learning. While productivity videos aim to promote efficient study and lifestyle habits, concerns exist that they may portray unrealistic standards and adversely impact self-image. The purpose is to assess viewer attitudes toward productivity-centered videos and use these insights to inform more meaningful digital educational content.

Prior work shows YouTube is extensively used for educational purposes, but content quality varies, particularly when produced by non-experts or influencers. Various post hoc approaches assess video quality (e.g., Journal of the American Medical Association criteria, video power index, thoroughness, view count, duration), leading to inconsistent evaluations even for academic content. Social media use can influence self-esteem and body image; exposure to high-achieving portrayals may contribute to anxiety or feelings of inadequacy. The authors also discuss the concept of social facilitation as a potential mechanism for why watching others being productive can motivate viewers. They propose exploring standardized, elective pre-publication peer review for educational videos to enhance trust and quality, acknowledging potential trade-offs with creator autonomy and timeliness.

Design: Cross-sectional observational survey conducted via social media. Ethics: IRB approval deemed unnecessary (minimal risk, anonymous). Recruitment: An open Qualtrics survey link was distributed via public YouTube Community Posts and Instagram Stories from four authors’ accounts; posts were accessible for 24 hours. Platforms did not track individual users who clicked links, preserving anonymity. Participants: Global audience; respondents provided demographics (gender, age, country, education level). Flow: Respondents first indicated if they use YouTube to supplement education (No ended survey). If Yes, they were asked if they have seen productivity-related videos. Measures: Emotions elicited by productivity videos (anxious, motivated, inspired, neutral/indifferent, inadequate); perceived helpfulness (1–10 scale, 10 most helpful); free-response items on why videos elicited feelings and how to improve them. Data handling: Incomplete responses were excluded; implausible ages removed. Statistical analysis: Descriptive statistics (counts, percentages, means/SD). Chi-square analysis to test homogeneity of categorical emotional responses, including age-based comparisons (<23 vs ≥23 years). Visualization included Sankey diagram, histograms, bar charts, and maps.

- Responses: 817 total received; 242 excluded (incomplete or inaccurate), leaving 575 completed responses for the main analysis. A separate summary noted 595 total responses across 60 countries in an earlier descriptive section. - Usage: Of the 575 completers, 544 (94.6%) used YouTube to supplement education; among them, 475 (87.3%) had seen productivity-related videos. - Demographics: Mean age 23 years (SD 5.46); higher proportion female (389 female, 174 male, 12 preferred not to say/non-binary). Education skewed toward higher levels. Majority from the United States. - Helpfulness: Perceived helpfulness median 7/10 (overall negative skew); average reported as 6.8/10 in the abstract summary. - Emotions (from the earlier 595-response dataset: among 494 who watched productivity videos): 357 motivated, 308 inspired, 127 anxious, 95 neutral/indifferent, 97 inadequate. - Age-based differences: Younger viewers (<23) were significantly more likely to feel anxious than older viewers (≥23) after watching productivity videos (28.6% vs 16.2%; chi-square = 8.554; p = 0.0034). - Free-response themes: Calls for increased transparency and realism (showing struggles, downtime, and failures), conciseness/shorter videos, relatable routines, and guidance on what to do when goals are not met.

Findings indicate that productivity-focused YouTube videos generally foster positive emotions (motivation and inspiration) and are perceived as moderately helpful by students and trainees. This supports the notion that observing others’ productive behaviors can encourage viewers, aligning with social facilitation effects. However, a meaningful minority—particularly younger viewers—experience anxiety or inadequacy, suggesting potential adverse impacts when content portrays unrealistic or idealized productivity without acknowledging setbacks or rest. The study’s insights are relevant for creators and educators: emphasizing relatable, transparent narratives, including failures, rest periods, and strategies for unmet goals, may enhance benefits and mitigate harms. The authors also highlight the need for better quality assurance of educational videos, proposing an elective pre-publication peer-review mechanism to bolster trust and accuracy without constraining creator autonomy.

Most viewers report feeling motivated or inspired by YouTube productivity videos, and they rate them as moderately helpful. Nonetheless, some viewers feel anxious or inadequate, often citing unrealistic expectations, lack of relatability, and insufficient guidance on handling unmet goals. Future productivity content should feature more realistic routines, include downtime and struggles, and offer practical strategies for adjusting when goals are missed. The authors also suggest exploring optional peer review for educational videos to improve quality and viewer confidence.

- Sampling via social media likely biased the sample toward followers/subscribers of the authors and users within specific niches (e.g., medicine/health), potentially inflating positive perceptions. - Self-selection bias and limited post availability (24-hour posts/stories) may affect representativeness. - Cross-sectional, self-reported perceptions cannot establish causality. - Exclusion of incomplete responses and removal of implausible ages may influence composition. - Potential discrepancies between preliminary tallies and finalized analytic sample sizes reported within the paper.