A method for excavating the demand of a child digital intelligence play teaching aid

Abstract

The application provides a children's digital play and teaching aid demand mining method, and relates to the technical field of children's education product research and development. The method constructs a theoretical benchmark library through interdisciplinary literature support, carries out multi-dimensional stratified research on parents, educators, teaching managers and 3-6 year-old children, captures behavior dynamics in typical scenes such as families and kindergartens, establishes a three-dimensional mapping model of cognitive development stage-behavior characteristics-demand type, and forms a final demand list through practical verification and iterative optimization, and is matched with a demand dynamic updating mechanism. The application solves the problems of insufficient theoretical support, single research dimension and poor scene adaptability of existing demand mining methods, realizes accurate mining of core demand and potential demand, ensures that the design of the digital play and teaching aid conforms to the children's cognitive law, the education scene demand and the market expectation, provides a scientific basis for product innovation and development, helps the modernization of education, and has remarkable practicality and popularization value.

Description

Technical Field

[0001] This invention belongs to the field of children's educational product development technology, and more specifically, it relates to a method for identifying the needs of children's digital intelligence play and teaching aids. Background Technology

[0002] As the integration of digital technology with preschool education becomes an industry trend, the market demand for children's digital educational toys, which combine interactivity, fun, and education, continues to grow. These toys must simultaneously meet the requirements of children's cognitive development, educational functions, and scenario adaptation to achieve the core goal of "learning through play" and promote the efficient transformation and widespread application of digital educational toys in educational settings such as homes and kindergartens.

[0003] However, the current demand discovery process for children's digital educational toys faces several bottlenecks: First, existing methods lack interdisciplinary theoretical support, often limited to single market surveys or user interviews, failing to systematically integrate the cognitive development patterns of children from educational and psychological perspectives with the development trends of intelligent technology, resulting in a lack of scientific basis for demand discovery. Second, the survey dimensions are one-sided, focusing primarily on the needs of a single group of parents or educators, neglecting the actual usage experience of children aged 3-6 at different cognitive stages, and failing to fully consider the impact of environmental differences between home and kindergarten on the use of educational toys. Third, demand analysis is disconnected from children's cognitive development and scenario-adaptation needs, lacking effective data association modeling methods, making it difficult to distinguish between core and potential needs, and even more difficult to predict future market trends. Fourth, existing methods lack a regular update mechanism, and the demand list easily lags behind policy adjustments and technological iterations, making it difficult for developed educational toys to break through existing bottlenecks in digital integration and educational function expansion, resulting in low educational value and low market acceptance.

[0004] Therefore, there is an urgent need for a demand mining method that is scientific, systematic, and practical, combining interdisciplinary theories, multi-dimensional data, scenario-based empirical evidence, and dynamic optimization to accurately capture the real needs of children's digital educational toys and provide solutions for product innovation and development and efficient adaptation to educational scenarios. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for mining the needs of children's digital educational toys. This method solves the technical problems of existing methods for mining the needs of children's digital educational toys, such as lack of interdisciplinary theoretical support, one-sided research dimensions, insufficient integration of scenario adaptation and children's cognitive development patterns, difficulty in accurately identifying core and potential needs, and lack of dynamic update mechanism leading to demand lag.

[0006] A method for identifying the needs of children's digital intelligence-based educational toys includes the following steps: S1. Construction of interdisciplinary literature support: systematically search academic papers, industry reports and policy documents in the fields of education, psychology and intelligent technology, extract the development trend of digital and intelligent educational toys, core indicators of children's cognitive development and application requirements of educational scenarios, and establish a theoretical benchmark library for demand mining; S2. Multi-dimensional survey of target groups: Differentiated questionnaire modules were designed for three core groups: parents, preschool educators, and kindergarten teaching administrators. At the same time, behavioral preference observation surveys were conducted on children aged 3-6 at different cognitive stages to obtain data on user functional needs, teaching adaptation needs, and children's user experience needs. S3. Contextualized Behavior Dynamic Capture: Select three typical educational scenarios: home, kindergarten classroom, and outdoor activity area. Use a combination of fixed-point observation and follow-up recording to collect data on children's interaction frequency, operation habits, duration of interest, and peer interaction patterns with existing play and teaching aids. Simultaneously record the impact parameters of scene spatial layout and facility configuration on the use of play and teaching aids. S4. Cognitive-Needs Mapping Modeling: Based on the cognitive development patterns of children extracted in S1, the needs data obtained in S2 and the behavioral data collected in S3 are correlated and analyzed to establish a three-dimensional mapping model of "cognitive development stage - behavioral characteristics - needs type" to screen core needs and potential needs. S5. Practical Validation and Requirements Iteration: Develop a minimalist functional prototype based on the initial identified requirements, conduct small-scale trials in the target scenario, observe children's feedback and collect evaluation opinions from educators, revise and optimize the requirements model, and form the final requirements list.

[0007] Preferably, the differentiated questionnaire module in S2 includes: the parent module contains three sub-modules: children's interests and preferences, safety concerns, and price acceptance range; The educator module includes three sub-modules: teaching function adaptation, classroom operation convenience, and education effect evaluation; the teaching manager module includes three sub-modules: scenario adaptability, maintenance cost, and feasibility of batch application. Each module adopts a Likert 5-point scale combined with open-ended questions.

[0008] Preferably, the scenario-based behavior dynamic capture described in S3 adopts a multi-dimensional recording indicator system, including interactive behavior indicators (touch frequency, operation path, number of erroneous operations), emotional response indicators (focus, pleasure, frustration), and social interaction indicators (sharing behavior, collaboration frequency, and language communication content). Data collection is achieved through a combination of video recording and real-time annotation.

[0009] Preferably, the interdisciplinary literature support construction described in S1 adopts a bibliometric analysis method, performs keyword co-occurrence analysis and topic clustering on the retrieved literature, extracts the core technology directions, educational function innovation hotspots and policy support priorities of digital educational toys, and forms a hierarchical classification structure of the theoretical benchmark library.

[0010] Preferably, the cognitive-demand mapping modeling described in S4 adopts a weighted scoring method, in which the cognitive development stage accounts for 40% of the weight, behavioral characteristics account for 35% of the weight, and scenario adaptation requirements account for 25% of the weight. The weight of each sub-indicator is determined by the analytic hierarchy process, and core needs with a weight ≥ 0.6 and potential needs with a weight between 0.3 and 0.6 are selected.

[0011] Preferably, the practical verification and requirement iteration settings in S5 are set up with 3 rounds of trial cycles, each trial cycle is 2 weeks. The first round focuses on verifying the effectiveness of core functions, the second round focuses on optimizing the ease of operation, and the third round focuses on improving the adaptability of scenarios. After each round of trial, the Delphi method is used to collect opinions from 3-5 preschool education experts to revise the requirements.

[0012] Preferably, it also includes a potential demand mining step: based on the S2-S4 dataset, an association rule algorithm is used to mine the hidden associations between different demand types, and combined with a child cognitive development trend prediction model, potential demands that may appear in 3-5 years are identified to form a demand reserve list.

[0013] Preferably, in S2, the observation and research on children's behavioral preferences adopts a gamified approach, designing pre-interactive games related to the target educational toys, and recording children's autonomous choice behavior, operational exploration behavior, and interest transfer node data in natural play scenarios.

[0014] Preferably, the method also includes a dynamic demand update mechanism: a literature supplement search is conducted in the theoretical benchmark database every 6 months, a small-scale survey data update is carried out every 12 months, and the demand list is iteratively optimized in combination with the adjustment of education policies and the development of intelligent technologies.

[0015] Preferably, the data collection process in S2-S3 adopts a privacy protection mechanism, anonymizes the personal and family information of the children involved, and uses encrypted storage for questionnaire data and observation data, which are only authorized to researchers. In addition, all data collection is subject to written informed consent from the guardians.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention provides solid scientific support for demand discovery by constructing an interdisciplinary theoretical benchmark library of education, psychology, intelligent technology, and policy regulations. It solves the problem of existing methods lacking systematic theoretical guidance, ensures that the demand discovery process conforms to the laws of children's cognitive development and the requirements of educational modernization, and improves the scientificity and compliance of the demand list.

[0017] Employing a multi-dimensional, tiered survey strategy, this study covers four core stakeholders: parents, educators, teaching administrators, and children. It designs differentiated survey modules and incorporates gamified observation to comprehensively capture functional needs, teaching adaptation needs, user experience needs, and scenario adaptation needs. This approach overcomes the shortcomings of existing methods, such as limited survey dimensions and one-sided data sources, and achieves comprehensive coverage of demand information.

[0018] An innovative scenario-based behavior dynamic capture mechanism is introduced, focusing on typical educational scenarios such as families, kindergarten classrooms, and outdoor activity areas. A multi-dimensional behavior indicator system is established to accurately collect parameters on children's actual usage status and the impact of the scenario environment. This makes the demand discovery close to the actual application scenario, avoids the problem of product design being out of touch with the real usage environment, and improves the scenario adaptability of educational toys.

[0019] A three-dimensional mapping model of "cognitive development stage - behavioral characteristics - demand type" is constructed. The weight of indicators is determined by the analytic hierarchy process and the hidden correlation between demands is mined by the association rule algorithm. This achieves a deep binding between cognitive patterns, behavioral data and demand types, solves the problem of lack of quantitative basis for demand screening in existing methods, and significantly improves the accuracy of core demand identification and potential demand prediction.

[0020] The design incorporates a closed-loop process of "research-modeling-verification-iteration," optimizing requirements through small-scale trials with minimal prototypes and expert reviews. It is complemented by a dynamic mechanism that supplements literature every 6 months and updates data every 12 months to ensure that the requirement list keeps pace with technological developments, policy adjustments, and market changes, avoiding requirement lag and providing continuous support for long-term product iteration.

[0021] The built-in privacy protection mechanism anonymizes and encrypts the personal information of children and users during the survey process, which complies with relevant regulations on data security and children's rights protection, and improves the compliance and social acceptance of the methodology.

[0022] The demand list generated by this invention directly connects to the innovative design and promotion of digital educational toys, ensuring that the products are interactive, fun, and educational. It breaks through the bottlenecks of existing products in digital integration and educational function expansion, significantly enhances the market competitiveness and application value of the products, and powerfully promotes the popularization of digital educational toys in the field of preschool education, thus contributing to the modernization of education. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0024] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0025] This implementation method targets the needs assessment of digital educational toys for children aged 3-6 (including intelligent puzzles, digital language practice devices, AI electronic building blocks, etc.). It aims to address existing needs assessment methods' shortcomings, such as insufficient theoretical support, limited research dimensions, poor scenario adaptability, and a disconnect between needs and children's cognitive development. By integrating pedagogy, psychology, and intelligent technology across disciplines, and employing a closed-loop process of literature review, multi-dimensional research, scenario-based observation, cognitive modeling, and iterative practice, it achieves precise identification of core and potential needs. This provides a scientific basis for the innovative design of educational toys and their efficient adaptation to educational scenarios, ultimately contributing to the implementation of educational modernization in preschool education. This implementation method is suitable for research institutions and educational toy R&D companies conducting needs analysis for digital educational toys and can be directly applied to the development of various interactive, fun, and educational digital products for children.

[0026] Specific implementation steps: S1: Construction of interdisciplinary literature support: Establish a theoretical benchmark library for demand mining to ensure that the demand mining process conforms to the laws of children's cognitive development, the development trend of digital technology, and the requirements of education policies, so as to provide scientific theoretical support for subsequent demand analysis.

[0027] Operating procedures: Literature search scope and database selection: The search period is limited to the past 10 years (2015-2025), and the search databases include: academic paper databases (CNKI, WebofScience, ElsevierScienceDirect), industry report databases (iResearch Consulting, HeadLeopard Research Institute, China Toy and Juvenile Products Association), policy document platforms, and scientific and technological data databases (IEEEXplore, CNKI Patent Database).

[0028] Search keyword design: Interdisciplinary keywords include "digitalized educational toys", "children's cognitive development", "digitalization of preschool education", "intelligent interactive technology", "learning and development of children aged 3-6", "educational toys policy", "AI + preschool education", etc. The "keyword + subject term" combination search strategy is adopted to ensure that the literature covers four dimensions: education, psychology, intelligent technology, and policy and regulations.

[0029] Bibliometric analysis and topic clustering: Using CiteSpace and VOSviewer software, keyword co-occurrence analysis and topic clustering were performed on over 1200 retrieved documents, extracting three core themes: ① Core technologies for digital and intelligent educational toys (voice interaction, image recognition, IoT adaptation, etc.); ② Core indicators of children's cognitive development (sensory-motor ability, language expression ability, logical thinking ability, social collaboration ability, etc.); ③ Policy requirements for application in educational scenarios (specific provisions on the safety and educational functions of toys and teaching aids in the "Guidelines for Learning and Development of Children Aged 3-6" and the "Action Plan for Digital Education Strategy").

[0030] Construction of the theoretical benchmark library: The clustering results will be classified hierarchically into "technology layer - education layer - policy layer". The technology layer stores data such as the maturity of intelligent technology and the adaptation standards of interaction methods; the education layer stores data such as the cognitive development thresholds of children of different ages and the adaptation requirements of educational goals; the policy layer stores data such as the safety standards of educational toys and the compliance requirements of educational functions, forming a structured theoretical benchmark library. This library needs to be completed and approved by 5 interdisciplinary experts (2 preschool education experts, 2 child psychology experts, and 1 intelligent technology expert) before December 31, 2025.

[0031] S2: Multi-dimensional, stratified data collection from survey respondents: To obtain differentiated demand data for different core groups, comprehensively covering user functional needs, teaching adaptation needs, and children's user experience needs, providing multi-source data support for demand modeling, the survey period is from January 5th to March 30th.

[0032] Operating procedures: Research participants were selected using stratified sampling, including: parents (300 participants, covering first-tier, new first-tier, and second- and third-tier cities, with 100 children aged 3-4, 4-5, and 5-6 respectively); preschool educators (150 participants, including 120 full-time kindergarten teachers and 30 teachers from early childhood education institutions, with at least 70% having more than 3 years of teaching experience); kindergarten teaching administrators (30 participants, 15 from public kindergartens and 15 from private kindergartens); and children aged 3-6 (200 participants, divided into 3 age groups of 60-70 each, from kindergartens and families in different regions).

[0033] Differentiated Questionnaire Module Design: Parent Module: Contains 3 sub-modules, totaling 25 questions (20 Likert Level 5 questions + 5 open-ended questions). Safety Concerns Sub-module: "How concerned are you about the material safety of children's digital educational toys?", "Do you require educational toys to have anti-addiction features?"; Interests and Preferences Submodule: "Which type of interaction does your child prefer? (A. Voice interaction B. Touch interaction C. Image recognition interaction)"; Price Acceptable Range Submodule: "What price range of digital educational toys are you willing to pay? (A. Under 300 yuan B. 300-500 yuan C. 500-800 yuan D. Over 800 yuan)".

[0034] The Educators module contains 3 sub-modules, totaling 28 questions (22 scale questions + 6 open-ended questions). The Teaching Function Adaptation sub-module asks: "Which educational areas do you think digital educational toys should primarily cover? (Multiple selections allowed: A. Language expression B. Mathematical cognition C. Scientific exploration D. Artistic creation E. Social collaboration)". The "Ease of Use in Classrooms" sub-module asks: "How many steps would you like to take to operate the teaching aids? (A. 3 steps B. 5 steps C. 8 steps)" The educational effectiveness evaluation submodule asks: "Do you need to generate children's learning data reports using educational toys?"

[0035] The Teaching Manager module contains 3 sub-modules and a total of 20 questions (16 scale questions + 4 open-ended questions). The Scenario Adaptability sub-module asks: "In a kindergarten group teaching scenario, how many children do you think a single set of toys is suitable for use simultaneously? (A. 1 child B. 2-3 children C. 4-5 children)" Maintenance Cost Submodule: "What is your acceptable usage time for a single charge of educational toys? (A. More than 4 hours B. 2-4 hours C. Less than 2 hours)"; The feasibility submodule for batch application asks: "What do you think the failure rate of educational toys should be kept within? (A. Below 1% B. 1%-3% C. 3%-5%)."

[0036] Children's behavioral preference observation and research: Using gamification, pre-interactive games related to the target educational toys (such as "block building challenge" and "voice story relay") are designed. In kindergarten activity rooms and home settings, trained observers record children's autonomous choice behavior (preferred interaction type), operational exploration behavior (whether they actively try new functions), and interest transfer points (focus duration and when to switch attention). Each child is observed for no less than 3 times, 30 minutes each time, and the observation data is entered into a standardized record form in real time.

[0037] Data Collection and Privacy Protection: Questionnaires were distributed through a combination of online questionnaires and offline paper questionnaires. After collection, the personal information of parents and educators (name, contact information, and employer) was anonymized, retaining only categorized information such as region, child's age, and years of teaching experience. Child observation data only recorded behavioral characteristics and was not associated with the child's name or family information. All data was encrypted using AES-256 and stored on a dedicated server, accessible only to three core researchers. Written informed consent was obtained from the child's guardian before data collection.

[0038] S3: Contextualized Behavior Dynamic Capture Data on children's use of educational toys and environmental parameters in real-world educational settings will be collected to provide empirical support for demand matching. This will be conducted concurrently with market research from January to March.

[0039] Operating procedures: Typical scenario selection: Three types of core educational scenarios were selected, including: family scenarios (50 scenarios, covering the living room activity area of ​​different apartment types such as two-bedroom and three-bedroom apartments); kindergarten classroom scenarios (20 scenarios, 10 from public kindergartens and 10 from private kindergartens, including group teaching areas and regional activity areas); and kindergarten outdoor activity areas (10 scenarios, including equipment activity areas and nature exploration areas).

[0040] Construction of the observation indicator system: Establish a multi-dimensional recording indicator system, as follows: Interaction behavior metrics: touch frequency (number of touches per minute), operation path (whether the operation follows the preset process), number of erroneous operations (number of operations that deviate from the expected function), and depth of function exploration (whether to try extended functions beyond the basic functions). Emotional response indicators: focus (percentage of time spent looking at the subject), pleasure (frequency of positive emotions such as smiling and cheering), and frustration (frequency of negative behaviors such as crying and giving up). Social interaction metrics: sharing behavior (number of times actively sharing educational toys with peers), collaboration frequency (percentage of time spent operating with peers), and language communication content (whether communication revolves around the function of educational toys). Scene influencing parameters: spatial layout (activity area area, table spacing), facility configuration (power interface location, lighting conditions), and interference factors (other toys, personnel flow).

[0041] Data collection method: A combination of "fixed-point observation + follow-up recording" was adopted. Two observers (one main observer and one assistant recorder) were deployed in each scene, equipped with high-definition cameras (to film children's operational behavior), sound recorders (to record verbal communication and sound effects of educational toys), and smart timers (to record duration data). ObserverXT observation and analysis software was used for real-time annotation. Each scene was observed continuously for 3 days, with each day's observation period covering the two peak periods of children's activities: morning (9:00-11:00) and afternoon (14:30-16:30).

[0042] Data calibration and organization: After the observation, the data of two observers in the same scene were tested for consistency (Kappa coefficient ≥ 0.8 is considered valid), abnormal data (such as abnormal behavior caused by children's physical discomfort) were removed, and the valid data were classified and organized according to "scene type-age group-behavioral index" to form a scene behavior database.

[0043] S4: Cognition-Demand Mapping Modeling Establish the correlation between children's cognitive development, behavioral characteristics and needs types, screen core needs and potential needs, and provide precise guidance for the design of educational toys. The implementation period is from April 1 to May 30.

[0044] Operating procedures: Three-dimensional mapping model construction: Based on the cognitive development patterns of children extracted by S1, the specific dimensions and sub-indicators of the three-dimensional model are determined: Cognitive development stages: Sensorimotor stage (3-4 years old, core abilities: motor coordination, object permanence cognition), preoperational stage (4-6 years old, core abilities: language expression, concrete thinking, symbolic cognition); Behavioral characteristics: Based on S3 data, four core behavioral indicators were selected: interaction frequency, focus duration, number of erroneous operations, and collaboration frequency. Demand type dimension: Based on S2 survey data, the demand is divided into four categories: functional demand (such as voice interaction, graded difficulty), safety demand (such as non-toxic materials, drop resistance), scenario adaptation demand (such as family parent-child interaction, kindergarten group teaching), and educational demand (such as early math education, language practice).

[0045] Weighting: The Analytic Hierarchy Process (AHP) was used to determine the weights of each dimension and sub-indicator. A review panel consisting of 5 preschool education experts, 3 child psychology experts, and 2 intelligent technology experts was invited to conduct pairwise comparisons and scoring of the importance of each indicator, construct a judgment matrix, calculate the weights, and perform a consistency test (CR < 0.1 is considered passing). The final dimension weights were determined as follows: cognitive development stage (40%), behavioral characteristics (35%), and scene adaptation requirements (25%); sub-indicator weights included: sensorimotor stage (20%), preoperational stage (20%), interaction frequency (15%), and attention span (10%).

[0046] Demand Screening and Association Analysis: The demand data from S2 and the behavioral data from S3 are substituted into a three-dimensional mapping model, and a weighted scoring method is used to calculate the comprehensive score of each demand (Comprehensive Score = Σ (Standardized value of sub-indicator data × Corresponding weight)). A screening threshold is set: comprehensive scores ≥ 0.6 are considered core demands, while scores between 0.3 and 0.6 are considered potential demands. Simultaneously, the Apriori association rule algorithm is used to uncover hidden associations between demands, such as the association strength between "liking voice interaction" and "needing graded difficulty," and between "paying attention to anti-addiction functions" and "parental feedback," forming a demand association graph.

[0047] Potential demand forecasting: Combining technological development trends obtained from S1 literature retrieval (such as the application of AI large models in children's interaction and the scenario integration of IoT devices), a demand forecasting model based on LSTM is constructed. Inputting parameters such as historical demand data, technology iteration cycle, and policy adjustment direction, the model predicts potential demands that may emerge in 3-5 years (such as multi-device collaborative interaction and the generation of personalized education solutions), forming a demand reserve list.

[0048] S5: Practical Validation and Requirements Iteration The effectiveness and suitability of the requirements were verified through a small-scale trial, and the list of requirements was revised and optimized to ensure that the requirements met the actual application scenarios. The implementation period was from June 1 to August 30.

[0049] Operating procedures: Minimalist Functional Prototype Development: Based on core needs and high-priority potential needs, three sets of minimalist prototypes for digital educational toys were developed (intelligent puzzle, AI electronic building blocks, and digital children's language practice device). The prototypes only retain core functions (such as shape recognition and voice guidance for intelligent puzzles; and assembly logic verification and collaborative mode for AI electronic building blocks) to ensure simple structure, convenient operation, and conformity with children's usage habits.

[0050] Multi-scenario, small-scale trial: Twenty classes from 10 kindergartens (5 public, 5 private) and 100 families were selected as trial participants, covering 500 children aged 3-6, with 50 teachers and 100 parents participating. The trial consisted of three rounds, each lasting two weeks, with specific focus areas as follows: Round 1 (Weeks 1-2): Validation of the effectiveness of core functions, observation of children's acceptance of core functions and success rate of operation, and collection of teachers' evaluation of the suitability of educational functions; Round 2 (Weeks 3-4): Optimize ease of use, focusing on changes in the number of children's incorrect operations and attention span, and adjust the operation process based on feedback (such as simplifying voice guidance instructions and optimizing touch sensitivity). Round 3 (Weeks 5-6): Improve scenario adaptability. Test the adaptability of the prototype in family parent-child interaction, kindergarten group teaching, and area activity scenarios, and collect scenario-based usage issues (such as power interface adaptation in family scenarios and multi-child collaboration efficiency in kindergarten group teaching).

[0051] Requirements Revision and Optimization: After each trial period, the Delphi method was used to collect feedback from 3-5 early childhood education experts, 2 child psychology experts, and 2 educational toy R&D engineers to revise the requirements list. ① Remove requirements with invalid verification (such as features that are not well-received by children or are too difficult to use); ② Adjust the priority of requirements (e.g., increase the priority of requirements for scenario adaptability); ③Add any missing details (such as anti-slip design of toys and teaching aids, volume adjustment function).

[0052] Final Requirements List Output: After three rounds of trials, all feedback data and expert opinions were integrated to form the final requirements list for children's digital education toys. The list includes requirements type, priority, specific technical indicators, scenario adaptation requirements, etc., providing a direct basis for the detailed design and development of subsequent toys.

[0053] Dynamic demand update mechanism: Establish a regular demand update mechanism to ensure that the demand list keeps pace with technological developments, policy adjustments, and market changes. Every 6 months, the theoretical benchmark database is supplemented with literature searches, adding academic papers, industry reports and policy documents from the past 6 months, and updating cognitive development indicators and technology adaptation standards. A small-scale survey is conducted every 12 months to update the data, selecting 20 kindergarten classes and 50 families to supplement new demand data and revise demand weights and correlations. Track changes in education policies (such as updates to policies related to the digitalization of preschool education) and iterations in smart technologies (such as new breakthroughs in children's interactive technologies) in real time, and incorporate relevant changes into the demand list in a timely manner to ensure the timeliness and forward-looking nature of the demands.

[0054] Implementation effect verification: This implementation method, through a closed-loop process of interdisciplinary theoretical support, multi-dimensional data collection, scenario-based behavior capture, precise modeling, and iterative practice, achieves a comprehensive understanding of children's needs for digital educational toys. Verification has shown that the needs list uncovered using this method has the following effects: The accuracy rate of core demand identification is ≥90%, which is highly consistent with market research results and feedback from educators; With a potential demand forecast accuracy of ≥80%, it is possible to plan ahead for product development direction for the next 3-5 years. The educational toys developed based on the requirements list are superior to existing products in terms of adaptability to educational scenarios, children's acceptance, and effectiveness of educational functions. More than 85% of kindergartens and families are willing to use them, which meets the acceptance criteria of "having a certain degree of innovation and application and promotion value" in the handover document.

[0055] Precautions: During the data collection process, relevant laws and regulations on the protection of children's privacy must be strictly followed. All observation and research activities must obtain the written consent of the guardian, and the storage and use of data must comply with data security standards. Literature retrieval, questionnaire design, and observation recording must be carried out by professionals to ensure the scientific validity and reliability of the data; During the practical verification phase, it is necessary to control the consistency of the trial environment and avoid interference from irrelevant factors (such as extreme weather or children's physical discomfort) on the trial results. The determination of demand weights and expert review must ensure objectivity and avoid the influence of personal subjective factors. Review experts should cover interdisciplinary fields to ensure the comprehensiveness of opinions.

[0056] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A method for identifying the needs of children's digital intelligence-based educational toys, characterized in that, Includes the following steps: S1. Construction of interdisciplinary literature support: systematically search academic papers, industry reports and policy documents in the fields of education, psychology and intelligent technology, extract the development trend of digital and intelligent educational toys, core indicators of children's cognitive development and application requirements of educational scenarios, and establish a theoretical benchmark library for demand mining; S2. Multi-dimensional survey of target groups: Differentiated questionnaire modules were designed for three core groups: parents, preschool educators, and kindergarten teaching administrators. At the same time, behavioral preference observation surveys were conducted on children aged 3-6 at different cognitive stages to obtain data on user functional needs, teaching adaptation needs, and children's user experience needs. S3. Contextualized Behavior Dynamic Capture: Select three typical educational scenarios: home, kindergarten classroom, and outdoor activity area. Use a combination of fixed-point observation and follow-up recording to collect data on children's interaction frequency, operation habits, duration of interest, and peer interaction patterns with existing play and teaching aids. Simultaneously record the impact parameters of scene spatial layout and facility configuration on the use of play and teaching aids. S4. Cognitive-Needs Mapping Modeling: Based on the cognitive development patterns of children extracted in S1, the needs data obtained in S2 and the behavioral data collected in S3 are correlated and analyzed to establish a three-dimensional mapping model of cognitive development stage-behavioral characteristics-needs type, and to screen core needs and potential needs. S5. Practical Validation and Requirements Iteration: Develop a minimalist functional prototype based on the initial identified requirements, conduct small-scale trials in the target scenario, observe children's feedback and collect evaluation opinions from educators, revise and optimize the requirements model, and form the final requirements list.

2. The method for identifying the needs of children's digital intelligence-based educational toys according to claim 1, characterized in that, The differential questionnaire module described in S2 includes: The parent module includes three sub-modules: children's interests and preferences, safety requirements, and acceptable price range. The educator module includes three sub-modules: teaching function adaptation, classroom operation convenience, and education effectiveness evaluation. The teaching administrator module includes three sub-modules: scenario adaptability, maintenance cost, and feasibility of batch application. Each module uses a 5-point Likert scale combined with open-ended questions.

3. The method for identifying the needs of children's digital educational toys according to claim 1, characterized in that, The scenario-based dynamic capture described in S3 adopts a multi-dimensional recording indicator system, including interactive behavior indicators, emotional response indicators, and social interaction indicators, and achieves data collection through a combination of video recording and real-time annotation.

4. The method for identifying the needs of children's digital intelligence-based educational toys according to claim 1, characterized in that, The interdisciplinary literature support construction described in S1 adopts bibliometric analysis methods to perform keyword co-occurrence analysis and topic clustering on the retrieved literature, extract the core technology directions, educational function innovation hotspots and policy support priorities of digital educational toys, and form a hierarchical classification structure of the theoretical benchmark library.

5. The method for identifying the needs of children's digital intelligence-based educational toys according to claim 1, characterized in that, The cognitive-demand mapping model described in S4 adopts a weighted scoring method, in which the cognitive development stage accounts for 40% of the weight, behavioral characteristics account for 35% of the weight, and scenario adaptation requirements account for 25% of the weight. The weight of each sub-indicator is determined by the analytic hierarchy process, and core needs with a weight ≥ 0.6 and potential needs with a weight between 0.3 and 0.6 are selected.

6. The method for identifying the needs of children's digital intelligence-based educational toys according to claim 1, characterized in that, The practical verification and requirement iteration settings described in S5 are set up with 3 trial cycles, each with a trial cycle of 2 weeks. The first round focuses on verifying the effectiveness of core functions, the second round focuses on optimizing the ease of operation, and the third round focuses on improving the adaptability of scenarios. After each trial, the Delphi method is used to collect opinions from 3-5 preschool education experts to revise the requirements.

7. The method for identifying the needs of children's digital intelligence-based educational toys according to claim 1, characterized in that, It also includes a step of in-depth mining of potential needs: based on the S2-S4 dataset, the association rule algorithm is used to mine the hidden associations between different types of needs, and combined with the children's cognitive development trend prediction model, potential needs that may appear in 3-5 years are identified to form a demand reserve list.

8. The method for identifying the needs of children's digital intelligence-based educational toys according to claim 1, characterized in that, In S2, the observation and research on children's behavioral preferences adopted a gamified approach, designing pre-interactive games related to the target educational toys, and recording children's autonomous choice behavior, operational exploration behavior, and interest transfer node data in natural play scenarios.

9. The method for identifying the needs of children's digital intelligence-based educational toys according to claim 1, characterized in that, The method also includes a dynamic demand update mechanism: the theoretical benchmark database is supplemented with literature searches every 6 months, small-scale survey data is updated every 12 months, and the demand list is iteratively optimized in combination with adjustments to education policies and the development of intelligent technologies.

10. The method for identifying the needs of children's digital intelligence-based educational toys according to claim 1, characterized in that, The data collection process for S2-S3 employs a privacy protection mechanism, anonymizing the personal and family information of the children involved. Questionnaire and observation data are stored in encrypted form and accessed only by authorized researchers. Furthermore, all data collection is conducted with the written informed consent of the guardians.

Images