You will be provided with a research report. The body of the report will contain some citations to references.

Citations in the main text may appear in the following forms:
1. A segment of text + space + number, for example: "Li Qiang constructed a socioeconomic status index (SES) based on income, education, and occupation, dividing society into 7 levels 15"
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Please identify **all** instances where references are cited in the main text, and extract (fact, ref_idx, url) triplets. When extracting, pay attention to the following:
1. Since these facts will need to be verified later, you may need to look for some context before and after the citation to ensure that the fact is complete and understandable, rather than just a simple phrase or short expression.
2. If a fact cites multiple references, then it should correspond to two triplets: (fact, ref_idx_1, url_1) and (fact, ref_idx_2, url_2).
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You should return a JSON list format, where each item in the list is a triplet, for example:
[
    {
        "fact": "Text segment from the original document. Note that Chinese quotation marks should use full-width marks. And add a single backslash before the English quotation mark to make it a readable for python json module.",
        "ref_idx": "The index of the cited reference in the reference list for this text segment.",
        "url": "The URL of the cited reference for this text segment (extracted from the reference list at the end of the research report or from the parentheses at the citation point)."
    }
]

Here is the main text of the research report:
## Executive Summary

A sports intelligent tutoring and learning guidance system driven by multimodal data fusion can be constructed as a sensing–fusion–decision–feedback loop, and versions of it have been prototyped or deployed in physical-education settings. The clearest engineering demonstration is a cloud–edge–end system that fuses 30 Hz video, 100 Hz inertial data and 1 Hz heart-rate data to deliver edge feedback in about 176–180 ms with 89.4–93.2% motion-recognition accuracy in a study of 45 college students [1].

School-scale deployment cases report Bluetooth gateways connecting smart jump ropes and heart-rate armbands across more than 100 schools [8], and platform cases report more than 100,000 users and 5 million accumulated data records [12].

The pedagogical case draws on intelligent-tutoring-system research, where meta-analyses report moderate positive effects for ITS versus teacher-led or textbook instruction [3] [10], but the advantage narrows against non-intelligent digital tutoring [6] and sports-specific evidence remains short-term, school-based, vendor-reported or benchmark-only [2] [9] [8] [13] [14].

The practical conclusion is that construction is feasible and partially operational, while application claims require stronger independent validation of device validity, privacy, usability, teacher integration and longitudinal learning outcomes [1] [7] [11] [2].

## 1. Framing: a tutoring system, not merely a sensor network

The topic is best understood as an intelligent tutoring system adapted to physical education, rather than as a stand-alone fitness tracker. An ITS is defined as software that interacts with students turn by turn and personalizes each turn using a learner model, often supported by probabilistic student models such as Bayesian Knowledge Tracing [10]. Earlier ITS research emphasizes real-time cognitive diagnosis and adaptive remediation as the core tutoring functions [3]. In physical education, these functions are translated into movement correction, exercise-load adjustment, personalized training prescriptions and safety monitoring [1] [9] [12].

The framing is also policy-driven. One analysis grounds “learn, practice, compete” integration in the 2022 compulsory-education physical education and health curriculum standard and the “Healthy China 2030” plan [11]. A platform case adds the “14th Five-Year” sports development plan, student physical-health management guidance, Education Informatization 2.0 and the State Council’s “AI+” action opinion [12]. A digital-base study for physical education identifies weak infrastructure and fragmented technology application as current problems, and proposes “learn, practice, compete, assess” as the organizing application scenario [4].

This framing matters because the system’s value is not only in recording motion or heart rate. The cited architectures aim to convert multimodal data into instructional decisions: edge feedback for immediate movement correction, cloud analysis for long-term personalization, dashboards for teacher intervention and recommendation engines for individualized exercise plans [1] [7] [12].

## 2. Construction: layered architecture, sensing stack and feedback loop

The most complete construction evidence is a three-tier cloud–edge–end system. Terminal devices collect multimodal data, edge nodes perform lightweight real-time analysis and instant feedback, and the cloud handles large-scale processing, deep model training and long-term personalized analysis [1]. The terminal layer in that prototype uses Logitech C920 Pro cameras at 30 Hz, Bosch BNO055 IMUs at 100 Hz and Polar H10 chest straps at 1 Hz [1]. The same study reports that its sub-200 ms average response is more than 65% lower than a pure-cloud solution [1].

| Layer | Cited construction pattern | Evidence |
| --- | --- | --- |
| Terminal sensing | 30 Hz video, 100 Hz IMU and 1 Hz heart-rate data in the cloud–edge–end prototype | [1] |
| Edge decision | lightweight motion recognition and instant feedback, with average edge response of 176–180 ms | [1] |
| Cloud decision | large-scale data processing, model training and long-term personalized analysis | [1] |
| School platform | four business modules plus two base platforms, with multi-agent coordination | [12] |
| Deployed connectivity | Bluetooth gateways connect smart jump ropes and heart-rate armbands, with one-second heart-rate sampling | [8] |

Other sources describe compatible but differently layered architectures. A digital-base study proposes four layers: perception input, data parsing, intelligent interaction and feedback optimization [4]. An IoT-based intelligent PE classroom paper describes a five-layer data flow: acquisition, transmission, processing, optimization/analysis and application [7]. A campus sports platform describes a “4+2” structure: sports classroom, after-school exercise, youth competition and physical-health management, supported by a campus smart-sports platform and a sports big-data platform [12].

The sensing stack is multimodal by design. The cloud–edge–end prototype combines vision, inertial and physiological streams [1]. The IoT conceptual system adds heart-rate variability, oxygen saturation, body temperature, acceleration, velocity, stride frequency, posture angles, ambient conditions and impact forces [7]. The deployed jump-rope case uses smart ropes, heart-rate armbands and Bluetooth gateways, with real-time display of instant heart rate and class mean, maximum and minimum heart rate [8]. The platform case uses cameras, body-composition and fitness-test instruments to collect video, steps, calories and motion trajectories [12].

Deployment status differs across sources. The cloud–edge–end system is an experimental prototype involving 45 college students [1]. The IoT paper explicitly states that no physical hardware was built and that it used 1,000 samples from a public dataset [7]. The Cassia case reports operational deployment in more than 100 primary and secondary schools, university sports departments and laboratories [8]. The platform case reports deployment in Tsinghua-affiliated and Beijing primary schools, with more than 100,000 users, 5 million accumulated records, more than 500,000 monthly active data points and 2 million completed exercise tasks [12].

## 3. Multimodal fusion: technical promise, benchmark dependence and classroom constraints

The most complete construction evidence is a three-tier cloud–edge–end system. Terminal devices collect multimodal data, edge nodes perform lightweight real-time analysis and instant feedback, and the cloud handles large-scale processing, deep model training and long-term personalized analysis [1]. The terminal layer in that prototype uses Logitech C920 Pro cameras at 30 Hz, Bosch BNO055 IMUs at 100 Hz and Polar H10 chest straps at 1 Hz [1]. The same study reports that its sub-200 ms average response is more than 65% lower than a pure-cloud solution [1].

Other sources describe compatible but differently layered architectures. A digital-base study proposes four layers: perception input, data parsing, intelligent interaction and feedback optimization [4]. An IoT-based intelligent PE classroom paper describes a five-layer data flow: acquisition, transmission, processing, optimization/analysis and application [7]. A campus sports platform describes a “4+2” structure: sports classroom, after-school exercise, youth competition and physical-health management, supported by a campus smart-sports platform and a sports big-data platform [12].

The sensing stack is multimodal by design. The cloud–edge–end prototype combines vision, inertial and physiological streams [1]. The IoT conceptual system adds heart-rate variability, oxygen saturation, body temperature, acceleration, velocity, stride frequency, posture angles, ambient conditions and impact forces [7]. The deployed jump-rope case uses smart ropes, heart-rate armbands and Bluetooth gateways, with real-time display of instant heart rate and class mean, maximum and minimum heart rate [8]. The platform case uses cameras, body-composition and fitness-test instruments to collect video, steps, calories and motion trajectories [12].

Deployment status differs across sources. The cloud–edge–end system is an experimental prototype involving 45 college students [1]. The IoT paper explicitly states that no physical hardware was built and that it used 1,000 samples from a public dataset [7]. The Cassia case reports operational deployment in more than 100 primary and secondary schools, university sports departments and laboratories [8]. The platform case reports deployment in Tsinghua-affiliated and Beijing primary schools, with more than 100,000 users, 5 million accumulated records, more than 500,000 monthly active data points and 2 million completed exercise tasks [12].

Multimodal data fusion is presented as the technical mechanism that should make the system more robust than any single sensor. A human-action-recognition survey states that multimodal data leads to superior performance compared with a single modality, and that fusion aims to achieve better accuracy than a single modality [5]. It lists benefits such as enhanced signal-to-noise ratio, improved confidence, increased robustness, enhanced resolution and better precision [5]. It also notes that Transformers are naturally suited to multi-stream fusion because of non-specific embedding and dynamic attention, but that they require substantial computation and memory and are constrained by scarce large-scale multimodal datasets [5]. The survey emphasizes that the choice of fusion strategy depends on data characteristics and task requirements and needs empirical evaluation [5].

The cloud–edge–end prototype operationalizes fusion with sport-specific perception strategies. In basketball, it extracts key joint points from video and uses a lightweight motion recognition model to output motion scores and correction suggestions [1]. In gymnastics, it compares IMU and camera key-point sequences against a standard template using Euclidean-distance similarity [1]. In long-distance running, it emphasizes step frequency, heart rate and exercise intensity to verify training safety and real-time adjustment [1]. The system reports average edge response time of 176–180 ms, motion-recognition accuracy of 89.4–93.2%, stability index of 0.88–0.91 and user satisfaction of 4.3–4.6 points before cloud personalization is enabled [1]. After enabling cloud-based personalized analysis, accuracy increased by about 3.4 percentage points, response time decreased by 10.8 ms and satisfaction increased by 0.6 points [1]. Across five iterative rounds, response time fell from 192.4 ms to 176.2 ms and accuracy rose from 90.5% to 93.2% [1]. Under high-latency network plus high-complexity tasks, response time was 197.5 ms, accuracy was 89.6% and stability was 0.85 [1].

These results are encouraging, but they are not uniform. The same source states that recognition accuracy for complex movements in high-difficulty programs such as gymnastics is slightly lower than in basketball and long-distance running [1]. It also notes that in high-speed flips, IMUs are susceptible to instantaneous high-impact interference and spatiotemporal alignment with visual data becomes difficult [1]. The paper further states that current personalized training prescriptions are mainly based on exercise scores and physiological indicators and have not fully incorporated psychological states and environmental conditions [1].

The broader human-pose-estimation literature adds a caution about validation. A systematic review of deep-learning HPE in sport categorizes applications into movement skill analysis, action recognition, augmented coaching tools and officiating support [2]. It finds that bespoke multi-model algorithms are common and single-person pose estimation predominates [2]. Of 50 included studies, only 12 validated algorithms on live human participants, while 38 used datasets only [2]. Private datasets were used for training and validation in 21 studies, limiting reproducibility and generalizability [2]. The review also states that systems trained in laboratory-like environments may perform poorly in real settings [2].

The newest coaching-feedback models show how far multimodal generation has moved, but they remain benchmark systems. ExpertAF is a video-plus-pose vision-language model using InternVideo2, a PCT pose encoder and Llama-3-8B, trained on Ego-Exo4D-derived tuples for basketball, soccer and rock climbing [13]. It reports BLEU-4 44.9, METEOR 49.6, ROUGE-L 54.6, recall@50 19.1, median rank 158 and PA-MPJPE 135, outperforming baselines including LLaVA-FT with pose, PoseScript/Fix and InternVideo2-NN [13]. Its commentary-generation gains are reported as statistically significant at p < 0.05 [13]. Ablations show that temporal alignment matters most: removing alignment drops BLEU-4 to 42.0, while removing pose or video gives smaller declines [13]. Human raters on a 1–4 Likert scale rated ExpertAF highest at 3.2 for commentary generation, 3.2 for demonstration retrieval and 2.4 for pose generation, compared with roughly 1.1–2.1 for baselines [13]. The paper also states limitations: it assumes one active person, leaves expert video generation for future work, notes noisy auto-generated 3D poses and shows a failure case in pinpointing the exact mistake [13].

BioCoach is a biomechanics-grounded streaming fitness-coaching framework that fuses appearance with 3D skeletal kinematics through an exercise-specific degree-of-freedom selector, morphometric context, motion-quality context and vision–biomechanics cross-attention [14]. On its newly created QEVD-bio-fit-coach benchmark, BioCoach reports METEOR 0.312, ROUGE-L 0.302, BERTScore 0.877, LLM-Accuracy 3.12, LLM-Bio-Accuracy 3.26 and T-F-Score 0.544, compared with Stream-VLM at 0.086, 0.108, 0.852, 1.86, 1.72 and 0.530 [14]. The largest judge gain is LLM-Bio-Accuracy at +89.5% [14]. Ablations show that removing Motion Quality Context collapses LLM-Bio-Accuracy from 3.26 to 2.04, while removing DoF selection or morphometric context causes smaller declines [14]. Reducing the temporal window from 3 s to 2 s maintains text quality but drops T-F-Score from 0.544 to 0.416 [14].

A crucial tension is visible in BioCoach’s own fair-comparison experiment. On the original QEVD-fit-coach benchmark, trained without its biomechanical labels, BioCoach reports METEOR 0.129, ROUGE-L 0.122, BERTScore 0.864 and LLM-Accuracy 2.56 versus Stream-VLM’s 0.127, 0.112, 0.863 and 2.45, but its T-F-Score is 0.544 versus Stream-VLM’s 0.56 [14]. The paper’s stated limitations are equally important: performance depends on 3D skeleton and shape quality, occlusions, loose clothing and extreme viewpoints can distort kinematics, and curated reference trajectories may miss exercise variants or adaptive forms [14]. Future work includes multi-sensor fusion and kinetic reasoning over joint reaction forces, muscle activation and injury-risk forces [14].

Taken together, the fusion evidence supports a layered conclusion. Fusion improves benchmark action recognition and can generate coaching text in research settings [5] [13] [14]. In classroom prototypes, fusion supports low-latency feedback and sport-specific assessment [1]. But the evidence does not show that benchmark coaching models have been integrated into validated school tutoring loops, because the coaching papers report dataset-level results [13] [14], while the school systems report operational deployment metrics [1] [8] [12].

## 4. Tutoring logic: learner models, recommendations and safety loops

The tutoring logic combines ITS principles with physical-education-specific control variables. ITS research emphasizes student modeling, hints, feedback and worked examples [3] [10]. A K-12 ITS meta-analysis using the MUTOS framework finds that worked-out examples are the most influential moderator of ITS effectiveness [10]. In PE, the analogous “worked example” appears as expert demonstration or movement template: the cloud–edge–end system compares student movement to a standard template in gymnastics and uses motion scores and correction instructions in basketball [1]. ExpertAF retrieves expert demonstrations and generates expert poses as visual corrections [13]. BioCoach generates phase-aligned, anatomy-specific cues grounded in kinematic constraints [14].

Adaptive recommendation is another layer. The digital-base study proposes an educational recommendation engine, embodied intelligent-agent collaboration and immersive scenario construction as key technologies [4]. A policy-oriented PE analysis describes structured knowledge graphs, personalized learning paths and performance analysis as the mechanism connecting learning, practice and competition [11]. The platform case describes reinforcement learning and recommendation algorithms generating personalized exercise prescriptions from historical data, fitness baselines and goals [12].

Safety and load control are central to the sports context. The cloud–edge–end system uses heart-rate data to adjust exercise intensity and avoid sports risk [1]. The IoT conceptual system includes HRV, SpO2, heart rate, body temperature, kinematics and environmental context [7]. The deployed jump-rope case monitors heart rate every second and displays class mean, maximum and minimum heart rate to support safe-interval decisions [8]. The platform case describes dynamic risk assessment and early warning for abnormal BMI, posture risk and heart-rate anomalies [12]. The school controlled report groups students by maximum heart rate and ability to adjust running distance and intensity [9].

Teacher-facing tools are part of the tutoring loop, not an optional add-on. The IoT paper describes a dashboard with physiological monitoring, movement/posture analysis, performance feedback classification, participation timelines, skill trends and an alert/recommendation engine [7]. The platform case describes a data cockpit at school, grade, class and individual levels [12]. The school controlled report describes teachers using data to adjust posture-correction drills, load groups and personalized plans [9]. This human-in-the-loop design is important because one HPE review warns that augmented coaching tools may remove the need for a coach, which could have long-term impacts on performance [2].

## 5. Application settings and deployment evidence

The application domain is broad but unevenly evidenced. K-12 PE is the most deployment-oriented setting. The Cassia case reports use in more than 100 primary and secondary schools, university sports departments and laboratories [8]. The platform case reports use in Tsinghua-affiliated and Beijing primary schools, with more than 100,000 users and 2 million completed exercise tasks [12]. The school controlled report describes 1,000 students across 20 classes, with half assigned to an experimental group and half to a control group [9].

University PE is represented by prototypes and conceptual models. The cloud–edge–end study involved 45 college students in basketball, gymnastics and long-distance running classes [1]. The IoT paper frames its work as college and university PE but explicitly states that no physical IoT hardware was built and that it used a public dataset [7]. The deployed case also includes university sports departments and professional training settings [8].

Athletic training, fitness and rehabilitation are emerging application areas. The HPE review identifies athlete development, movement skill analysis, action recognition, coaching tools and officiating as domains [2]. The BioCoach paper frames streaming fitness coaching as relevant to at-home workouts, injury prevention and data-driven rehabilitation [14]. The Cassia case reports use with professional teams and research laboratories [8].

The organizing pedagogical scenario across several sources is “learn, practice, compete, assess.” The digital-base study summarizes these as four major teaching application scenarios [4]. The platform case states that the system constructs an integrated “learn, practice, compete, assess” ecosystem across classroom teaching, home exercise, youth competition and fitness testing [12]. The policy analysis emphasizes “teach well, practice diligently, compete often” and connects AI devices to learning, practice and competition [11].

## 6. Effectiveness: what the evidence supports and what it does not

The effectiveness picture must be separated into general ITS evidence [3] [10], K-12 AI-ITS evidence [6], sports-specific PE evidence [9] and technical benchmark evidence [13] [14].

General ITS evidence is relatively strong. Ma and colleagues analyzed 107 effect sizes involving 14,321 participants and found that ITS were associated with greater achievement than teacher-led large-group instruction (g = 0.42), non-ITS computer-based instruction (g = 0.57) and textbooks or workbooks (g = 0.35) [3]. The random-effects overall weighted mean effect size was g = 0.41 [3]. However, ITS showed no significant difference compared with individualized human tutoring (g = −0.11) or small-group instruction (g = 0.05) [3]. The same meta-analysis cautions that ITS gains may reflect intensive instructional design rather than ITS-specific features, and that many studies did not report treatment duration or research design [3].

K-12 ITS evidence is positive but more modest. A 2025 meta-analysis of U.S. K-12 ITS reports an overall effect size of g = 0.271 across 18 studies, 77 effect sizes and 11 ITS [10]. It cites VanLehn’s step-based ITS effect of d = 0.76, sub-step d = 0.40 and answer-based d = 0.31, and Ma et al.’s comparisons of g = 0.44 versus large-group human instruction, g = 0.577 versus computer-based instruction and g = 0.36 versus textbooks [10]. It also finds lower effects in rural settings, with g = 0.146 in studies including rural locations versus g = 0.298 in urban settings [10]. Only 20% of effect sizes came from implementations lasting more than six months [10].

K-12 AI-ITS systematic review evidence reinforces the mitigation. A review of 28 studies with 4,597 students finds that ITS effects are generally positive but are mitigated when compared with non-intelligent tutoring systems [6]. Seven of eight studies comparing ITS with traditional or usual teaching reported significant positive effects, but one found no significant difference [6]. In comparisons with non-intelligent tutoring, only one of four studies showed ITS advantage, while three found no significant difference [6]. The longest intervention lasted 30 weeks, and the review calls for longer interventions and more diverse samples [6]. It also states that none of the included articles mentioned consideration of AI ethics [6].

| Evidence source type | Reported result | What it can support |
| --- | --- | --- |
| ITS meta-analysis | overall random-effects g = 0.41; g = 0.42 versus large-group human instruction | general ITS advantage over some traditional instruction [3] |
| K-12 ITS meta-analysis | g = 0.271 for U.S. K-12 ITS | positive but smaller effect in screened K-12 evidence [10] |
| K-12 AI-ITS review | positive but mitigated versus non-intelligent tutoring | ITS advantage may not exceed simpler digital tutoring [6] |
| School PE controlled report | +27% skill achievement, +35% interest | short-term school-based association [9] |
| Vendor case | +50% jump-score claim | deployment feasibility and vendor-reported outcomes [8] |
| Coaching VLM papers | ExpertAF BLEU-4 44.9; BioCoach LLM-Bio-Accuracy 3.26 | benchmark feedback quality, not classroom learning gains [13] [14] |

Sports-specific PE evidence is weaker and more heterogeneous. The school controlled report states that the experimental group’s sports-skill achievement rate increased by 27% and learning-interest scale scores increased by 35% [9]. In a 12-week gymnastics sub-experiment with two classes of 50 students, the experimental group’s average score rose from 45 to 78, while the control group’s rose from 46 to 60, with p < 0.01 [9]. In a 16-week heart-rate-guided running experiment with 100 students, maximum oxygen uptake rose from 35 to 42 ml/kg/min in the experimental group, about +20%, compared with 36 to 38 ml/kg/min in the control group, about +6%, with p < 0.01 [9]. Student satisfaction was 85% in the experimental group versus 60% in the control group [9].

The vendor case reports larger gains: 30% increased interest, 40% increased participation, 50% average jump-score improvement, 20% cardiopulmonary improvement, 90% teacher satisfaction and 60% teaching-efficiency improvement [8]. These are vendor-reported deployment outcomes, not independent controlled efficacy estimates, and they should be read alongside the review finding that no longitudinal studies empirically test the long-term effects of DL HPE tools in athlete development programs [8] [2]. The platform case reports more than 30% improvement in effective classroom exercise time and large usage volumes [12]. Again, these are implementation-case figures rather than controlled causal evidence [12].

The central tension is that engineering metrics are more mature than learning metrics. A prototype can report 89.4–93.2% motion-recognition accuracy and 176–180 ms edge response [1], while a systematic review finds no longitudinal efficacy studies for DL pose estimation in sport [2]. A K-12 ITS meta-analysis shows positive but short-term and context-dependent effects [10], while a K-12 AI-ITS review finds mitigation versus non-intelligent tutoring and an ethics gap [6]. The available evidence does not establish durable learning or health gains at scale, because the cited evidence base lacks longitudinal sports ITS efficacy studies and relies heavily on short-term, quasi-experimental, vendor or benchmark reports [2] [6] [9] [8] [13] [14].

## 7. Limitations, risks and open questions

The first limitation is evidence quality. The HPE review states that most studies rely on private datasets, limiting reproducibility and generalizability, and that lack of open datasets and standardized practices poses challenges for adoption [2]. It also notes that algorithmic metrics are often inaccessible and lack direct practical interpretation for sports-science practitioners [2]. The ITS meta-analysis cautions that many studies did not report treatment duration or research design [3]. The K-12 ITS meta-analysis uses MUTOS to examine internal and external validity and notes that researcher-developed measures may be vulnerable to construct validity threats [10]. The K-12 AI-ITS review calls for longer interventions, larger samples and more diverse populations [6].

The second limitation is device validity and data quality. A PE policy analysis explicitly questions the concurrent validity and acceptability of AI and wearable devices and calls for validation studies [11]. The cloud–edge–end prototype acknowledges high-impact interference in flips and lower accuracy in gymnastics [1]. The IoT paper notes noisy and erroneous sensor data and that its pedagogical metrics are computed rather than directly measured [7]. ExpertAF notes noisy auto-generated 3D poses and a failure case in pinpointing the exact mistake [13]. BioCoach notes dependence on 3D skeleton quality, occlusions, loose clothing and extreme viewpoints [14]. The HPE review notes that lab-trained systems may fail in real settings and that multi-person tracking remains difficult [2].

The third limitation is privacy, ethics and governance. The cloud–edge–end paper names privacy-security verification as an unresolved systematic challenge in large-scale teaching [1]. The IoT paper states that continuous monitoring involves sensitive physiological and behavioral information that must be protected by institutional regulations and laws [7]. The school controlled report recommends explicit authorization, encrypted transmission and storage, access control and data-privacy training [9]. The K-12 AI-ITS review states that none of the included articles mentioned consideration of AI ethics [6]. These sources converge on the need for governance, but they do not provide a common regulatory or technical standard for sports ITS.

The fourth limitation is equity, cost and teacher capacity. The digital-base study identifies weak infrastructure and fragmented technology application as current problems [4]. The IoT paper lists sensor calibration, implementation cost and network latency as practical adoption constraints [7]. The school report calls for lower-cost devices, government subsidies and teacher training in data collection, analysis and interpretation [9]. The K-12 ITS meta-analysis finds lower effects in rural settings than in urban settings [10]. The HPE review warns that automated coaching tools may remove the coach, with possible long-term performance impacts [2].

The fifth limitation is system integration. The cloud–edge–end paper states that existing research has limitations in systematic integration, including lack of end-to-end design from multimodal perception to intelligent decision-making, insufficient cloud–edge–end collaboration and inadequate verification of scalability and privacy-security in large-scale teaching [1]. Its own personalization module still does not fully incorporate psychological states and environmental conditions [1]. ExpertAF assumes a single active person and does not yet generate full expert videos [13]. BioCoach relies on curated reference trajectories and does not yet model kinetic forces or muscle activation [14]. The HPE review notes that single-person focus removes contextual factors important in many sports [2].

Several open questions remain in the subject’s own terms. Whether multimodal sports tutoring produces durable skill, health and motivation gains beyond short-term classroom effects is not settled by the cited evidence [2] [6] [10]. Whether existing sensor and ITS evaluation methods can be harmonized into a common validation protocol remains open, given calls for device validity, standardized practices and longer interventions [11] [2] [6]. Whether coaching-generation models can move from benchmark datasets to classroom loops with acceptable latency, privacy protection and teacher oversight remains open, given the separation between benchmark metrics and deployment metrics [13] [14] [1] [8] [12].

## 8. Concluding assessment

The strongest supportable answer is that a multimodal-data-fusion-driven sports intelligent tutoring and learning guidance system is constructible, and several components already exist in working or deployed forms. The architecture is well specified: terminal sensing, edge inference, cloud personalization, learner modeling, adaptive recommendation, safety monitoring and teacher dashboards [1] [4] [7] [12]. Real deployment evidence exists for Bluetooth-based jump-rope and heart-rate monitoring across more than 100 schools [8], and platform evidence exists for large user counts and data volumes [12]. Technical performance evidence exists for low-latency motion recognition and personalization gains in a college-student prototype [1]. Frontier coaching-generation evidence exists for multimodal video-plus-pose models and biomechanics-grounded feedback models [13] [14].

The weaker part is application efficacy. ITS research supports moderate benefits in cognitive domains [3] [10], but K-12 AI-ITS effects are mitigated against non-intelligent tutoring and under-evaluated ethically [6]. Sports-specific evidence is promising but limited: one controlled school report shows +27% skill achievement and +35% interest [9], while the HPE systematic review finds no longitudinal efficacy studies for DL pose estimation in sport [2]. Vendor and platform cases report large gains [8] [12], but they are not independent controlled studies. Coaching VLM papers report strong benchmark metrics [13] [14], but they do not establish classroom learning outcomes.

Therefore, the system should be built, but the build should be treated as a staged research-and-deployment program rather than as a finished evidence-backed intervention. A defensible next step is to combine the cloud–edge–end architecture [1], the digital-base and platform layers [4] [12], deployed gateway stacks [8] and coaching-generation models [13] [14] under a validation regime that includes device concurrent validity [11], privacy and security governance [1] [7] [9], teacher-centered evaluation [2] [9], open or standardized benchmarks [2], and longer-term controlled studies of skill, health, motivation and equity [6] [10]. The decisive uncertainty is not whether the system can be constructed; the cited engineering evidence largely settles that. The decisive uncertainty is whether the constructed system produces durable, equitable and safe learning outcomes at scale, and the available evidence does not yet establish that.

## References

[1] Multi-modal perception and decision-making system for smart sports classroom with cloud-edge-end collaboration | Discover Artificial Intelligence | Springer Nature Link — https://link.springer.com/article/10.1007/s44163-026-01082-w
[2] The Application of Deep Learning Human Pose Estimation in Sport: A Systematic Review | Sports Medicine - Open | Springer Nature Link — https://link.springer.com/article/10.1186/s40798-025-00953-3
[3] https://cs.uky.edu/~sgware/reading/papers/ma2014intelligent.pdf — https://cs.uky.edu/~sgware/reading/papers/ma2014intelligent.pdf
[4] 上海体育大学学报 — https://styb.cbpt.cnki.net/portal/journal/portal/client/paper/a9fcec8607e7ac3057b731336b32c258
[5] [2405.15813] From CNNs to Transformers in Multimodal Human Action Recognition: A Survey — https://ar5iv.labs.arxiv.org/html/2405.15813
[6] A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education - PMC — https://pmc.ncbi.nlm.nih.gov/articles/PMC12078640/
[7] Construction of intelligent PE classroom and innovation of teaching mode in colleges and universities based on machine learning and Internet of Things | Discover Internet of Things | Springer Nature Link — https://link.springer.com/article/10.1007/s43926-026-00301-1
[8] http://journals.viserdata.com/index.php/jscs/article/viewFile/16940/17163 — http://journals.viserdata.com/index.php/jscs/article/viewFile/16940/17163
[9] 硕尔达体育课堂跳绳训练与运动负荷实时监测案例研究 - 北京桂花网 — https://www.cassianetworks.com.cn/case-study/sedxl/
[10] https://arxiv.org/pdf/2511.04997 — https://arxiv.org/pdf/2511.04997
[11] 人工智能及其相关设备在‘学，练，赛’一体化教学中的嵌入——以体育教学为例 | 孙 | 体能科学 — http://journals.viserdata.com/index.php/jscs/article/view/18534
[12] AI校园体育平台——人工智能+教育体育融合创新示范案例 | 信息化观察网 - 引领行业变革 — https://www.infoobs.com/case/20260225/1017.html
[13] ExpertAF: Expert Actionable Feedback from Video — https://openaccess.thecvf.com/content/CVPR2025/papers/Ashutosh_ExpertAF_Expert_Actionable_Feedback_from_Video_CVPR_2025_paper.pdf
[14] From 3D Pose to Prose: Biomechanics-Grounded Vision-Language Coaching — https://openaccess.thecvf.com/content/CVPR2026/papers/Ji_From_3D_Pose_to_Prose_Biomechanics-Grounded_Vision-Language_Coaching_CVPR_2026_paper.pdf


Please begin the extraction now. Output only the JSON list directly, without any chitchat or explanations.