Study-section progressive AI ethic scene simulation teaching method and system

Abstract

The invention provides a teaching method and system for progressive AI ethic scene simulation in different learning segments, and the method comprises the steps: constructing a progressive AI ethic scene library in different learning segments according to different scene grades and corresponding task requirements; configuring a scene simulation platform and an AI ethic guidance module; based on the AI ethical scene library, the scene simulation platform and the AI ethical guidance module, progressive scene simulation and real-time task guidance are executed; in the process of executing scene simulation and real-time task guidance, collecting and analyzing multi-modal teaching data in real time; and based on an analysis result, generating a teaching evaluation and personalized promotion scheme. According to the AI ethical teaching system, the practicability and immersion of ethical teaching are improved, large-scale personalized teaching is realized, a scientific and comprehensive literacy evaluation standard is established, a data-driven teaching optimization closed loop is formed, and the AI ethical teaching system based on branch learning, scene simulation and intelligent guidance is constructed.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of artificial intelligence and educational science, specifically to a progressive AI ethics scenario simulation teaching method and system that is divided into different learning stages. Background Technology

[0002] With the increasing prevalence of artificial intelligence (AI) technology in primary and secondary school education, the importance of AI ethics education is becoming increasingly prominent. This includes core issues such as data privacy, algorithmic fairness, technology for good, and a sense of responsibility, all of which are of great significance in primary and secondary school education. However, existing methods of teaching AI ethics have the following significant shortcomings: Lack of adaptation to different learning stages: The teaching content often adopts a "one-size-fits-all" approach, failing to accurately match the cognitive development levels of students at different stages. Abstract concepts are taught to elementary school students, while the content lacks depth for high school students, resulting in poor teaching effectiveness.

[0003] The teaching methods are too simplistic: the teaching relies heavily on static case studies or video presentations, lacking dynamic and interactive scenarios. Students cannot personally experience the complexity and consequences of ethical decision-making, resulting in a serious disconnect between theory and practice.

[0004] Delayed guidance and feedback: The teaching process relies heavily on teacher-led instruction, making it difficult to achieve large-scale personalized tutoring. Students do not receive real-time feedback when facing ethical dilemmas, and teachers face a heavy workload of after-class grading with long feedback cycles.

[0005] The evaluation dimensions are one-sided: Most existing evaluation systems are limited to testing students' memorization of ethical knowledge points, neglecting the comprehensive assessment of students' "ethical decision-making ability", "logical expression ability" and "innovative problem-solving ability".

[0006] The teaching cases are often disconnected from real life: they are mostly derived from industrial or scientific research fields and lack scenarios that are close to the daily lives of primary and secondary school students (such as AI voice assistants and short video recommendation algorithms), resulting in students having a weak sense of immersion and low interest in learning.

[0007] Therefore, there is an urgent need in this field for an AI-powered ethics education solution that can achieve precise teaching across different grade levels, dynamic interaction, real-time guidance, and comprehensive evaluation. Currently, no descriptions or reports of technologies similar to this invention have been found, nor have similar domestic or international materials been collected. Summary of the Invention

[0008] To address the aforementioned shortcomings in existing technologies, this invention provides a segmented, progressive AI ethics scenario simulation teaching method and system.

[0009] According to a first aspect of the present invention, a segmented, progressive AI ethics scenario simulation teaching method is provided, comprising: Based on different scenario levels and corresponding task requirements, a progressive AI ethics scenario library is constructed for different learning stages. Configure a scenario simulation platform and an AI ethics guidance module; Based on the AI ​​ethics scenario library, scenario simulation platform, and AI ethics guidance module, progressive scenario simulation and real-time task guidance are performed. During the execution of scenario simulations and real-time task guidance, multimodal teaching data is collected and analyzed in real time; Based on the analysis results, teaching evaluation and personalized improvement plans are generated.

[0010] Preferably, the step of constructing a progressive AI ethics scenario library based on different scenario levels and corresponding task requirements includes: Based on the complexity of the scenarios at different learning stages, the scenarios are divided into elementary, intermediate, and advanced stages. For each graded stage, corresponding ethical scenarios and task requirements are designed. Each ethical scenario is matched with a dedicated teaching resource package, and ethical rules (including core principles such as data privacy protection, algorithmic fairness, and technology for good) are built into the scenario logic. By linking ethical scenarios with ethical resources, a progressive AI ethical scenario library is constructed, divided into different learning stages.

[0011] Preferably, the configuration scenario simulation platform and AI ethics guidance module include: It adopts a dual-mode approach of web and client, adapts the interactive logic and interface to different learning stages, and configures a scenario simulation platform; A pre-trained ethics guidance engine was built based on a large language model and deployed on a local server to ensure data security. A segmented guidance strategy is constructed for the aforementioned ethics guidance engine as an output constraint to ensure that the guidance content matches the cognitive level and teaching objectives of students at different learning stages.

[0012] Preferably, the pre-trained ethics guidance engine based on a large language model includes: Construct training samples, including: AI ethics cases, teaching guidance corpus, and samples of common student errors; The training samples were used to fine-tune the large language model, resulting in an ethics guidance engine with real-time question answering, progressive questioning, and error correction capabilities.

[0013] Preferably, the step of performing progressive scenario simulation and real-time task guidance based on the AI ​​ethics scenario library, scenario simulation platform, and AI ethics guidance module includes: Import an AI ethics scenario library into the scenario simulation platform and assign task roles to different learning stages; Based on the task requirements and the task roles, task decisions are made through the scenario simulation platform to form a dynamic and coherent narrative experience and obtain task simulation results. By combining the AI ​​ethics guidance module, the entire decision-making path and content are monitored, and the simulation process is intervened and guided based on the monitoring results (such as the detection of decision hesitation, logical errors or ethical inappropriateness).

[0014] Preferably, the step of collecting and analyzing multimodal teaching data in real time during the execution of scenario simulation and real-time task guidance includes: By embedding and recording data on the platform, multi-source data is collected during the execution of scenario simulations and real-time task guidance, including: decision data, interaction data, outcome data, and feedback data. The multi-source data is processed using a local analysis model (such as an analysis framework that combines rule engines and machine learning models) to obtain analysis results, including: cognitive diagnosis, capability assessment, and vulnerability identification.

[0015] Preferably, the step of generating teaching evaluation and personalized improvement plans based on the analysis results includes: Construct a multi-dimensional quantitative scoring system, and based on the analysis results, comprehensively evaluate students' performance from the cognitive, ability, and attitude dimensions; Based on the analysis results, the system automatically pushes targeted learning resources (including micro-lesson videos, interactive exercises, and extended reading materials). It provides teachers with data reports on both the class as a whole and individual students, and generates suggestions for improving teaching (including adjustments to course content, optimization of teaching strategies, and a list of students to focus on).

[0016] According to a second aspect of the present invention, a segmented, progressive AI ethics scenario simulation teaching system is provided, comprising: The database construction module is used to build a progressive AI ethics scenario database based on different scenario levels and corresponding task requirements; The simulation platform construction module is used to configure the scenario simulation platform and the AI ​​ethics guidance module. The teaching task execution module, based on the AI ​​ethics scenario library, scenario simulation platform and AI ethics guidance module, performs progressive scenario simulation and real-time task guidance. The multi-source data acquisition module is used to collect and analyze multimodal teaching data in real time during the execution of scenario simulation and real-time task guidance. The evaluation and suggestion module generates teaching evaluations and personalized improvement plans based on the analysis results.

[0017] By adopting the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art: This invention achieves true individualized instruction: through refined scenario design by grade level and stage, it ensures that the teaching content is accurately matched with the cognitive level of students at each grade level, greatly improving the effectiveness of teaching and student participation.

[0018] This invention enhances the practicality and immersion of ethics teaching: dynamic and interactive scenario simulations transform abstract ethical discussions into tangible and actionable hands-on experiences, significantly enhancing the internalization and application of knowledge.

[0019] This invention enables personalized teaching on a large scale: the local AI ethics guidance module can provide real-time, professional tutoring to a large number of students simultaneously, effectively reducing the burden on teachers and solving the problem of delayed feedback in traditional teaching.

[0020] This invention establishes a scientific and comprehensive standard for evaluating students' ethical qualities: it breaks through the limitations of single-knowledge assessment and comprehensively evaluates students' ethical qualities from multiple dimensions, providing a feasible technical tool for education that guides core competencies.

[0021] This invention forms a data-driven teaching optimization closed loop: by mining and analyzing teaching data throughout the entire process, it provides precise data support for teaching reflection and continuous improvement, fundamentally improving teaching quality and efficiency. Attached Figure Description

[0022] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the workflow of a segmented, progressive AI ethics scenario simulation teaching method according to a preferred embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the constituent modules of the segmented, progressive AI ethics scenario simulation teaching system in a preferred embodiment of the present invention.

[0024] Figure 3 This is a flowchart of the workflow of a segmented, progressive AI ethics scenario simulation teaching system in a specific application example of the present invention. Detailed Implementation

[0025] The embodiments of the present invention are described in detail below: These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0026] Existing AI ethics teaching methods suffer from problems such as lack of adaptability to different educational stages, monotonous scenario formats, delayed guidance and feedback, one-sided evaluation dimensions, and disconnect from real-life situations. To address these issues, one embodiment of this invention provides a grade-level progressive AI ethics scenario simulation teaching method. This method implements an AI ethics teaching technology based on grade levels, scenario simulation, and intelligent guidance, suitable for science and technology innovation education courses in primary and secondary schools, and is used to improve students' AI ethics awareness and decision-making abilities.

[0027] Specifically, such as Figure 1 As shown, the segmented, progressive AI ethics scenario simulation teaching method provided in this embodiment may include: S1. Construct a progressive AI ethics scenario library based on different scenario levels and corresponding task requirements; S2, configured with a scenario simulation platform and an AI ethics guidance module; S3, based on the AI ​​ethics scenario library, scenario simulation platform and AI ethics guidance module, performs progressive scenario simulation and real-time task guidance; S4 collects and analyzes multimodal teaching data in real time during the execution of scenario simulation and real-time task guidance. S5 generates teaching evaluation and personalized improvement plans based on the analysis results.

[0028] In some preferred embodiments, the above-mentioned S1, which constructs a progressive AI ethics scenario library based on different scenario levels and corresponding task requirements, may further include: S11. Based on the complexity of the scenarios at different learning stages, the scenarios are divided into elementary, intermediate, and advanced stages. S12, for each graded stage, designs corresponding ethical scenarios and task requirements; S13 assigns a dedicated teaching resource package to each ethical scenario and embeds ethical rules into the scenario logic, linking ethical scenarios with ethical resources to construct a progressive AI ethics scenario library divided by learning stages. The teaching resource package includes: scenario introduction short videos, knowledge point manuals related to curriculum standards (such as data privacy, algorithmic fairness, and intellectual property), role scripts, and decision-making reference materials; the ethical rules include: core ethical principles such as data privacy protection, algorithmic fairness, technology for good, and attribution of responsibility. Specifically, data privacy protection addresses the necessity of protecting students' names, grades, real-time location information, and interests, and the consequences of not protecting them. Algorithmic fairness addresses the possibility of unfair behavior arising from biased or incomplete AI training data.

[0029] In some preferred embodiments, the above-mentioned S2, which configures the scenario simulation platform and the AI ​​ethics guidance module, may further include: S21 adopts a dual-mode approach of web and client, adapting interactive logic and interface for different learning stages, and configuring a scenario simulation platform. S22, a pre-trained ethics guidance engine is built based on a large language model and deployed on a local server to ensure data security; among them, AI ethics cases, teaching guidance corpora and common student error samples are used to fine-tune the large language model to build an ethics guidance engine with real-time question answering, progressive questioning and error correction capabilities. S23. Construct grade-specific guidance strategies for the ethics guidance engine as output constraints to ensure that the guidance content matches the cognitive level and teaching objectives of students at different grade levels; for example, the primary school stage adopts the "prompt + encouragement" strategy, the junior high school stage adopts the "question + inspiration" strategy, and the senior high school stage adopts the "argumentation + reference" strategy.

[0030] In some preferred embodiments, the above-mentioned S3, based on the AI ​​ethics scenario library, scenario simulation platform, and AI ethics guidance module, performs progressive scenario simulation and real-time task guidance, and may further include: S31. Import the AI ​​ethics scenario library into the scenario simulation platform and assign task roles to different learning stages; S32, based on task requirements and task roles, task decisions are made through a scenario simulation platform to form a dynamic and coherent narrative experience and obtain task simulation results; S34, combined with the AI ​​ethics guidance module, monitors the decision-making path and content throughout the process, and intervenes and guides the simulation process based on the monitoring results (such as detecting decision hesitation, logical errors or ethical inappropriateness).

[0031] In some preferred embodiments, the above-mentioned S4, during the execution of scenario simulation and real-time task guidance, may further include real-time acquisition and analysis of multimodal teaching data, and may also include: S41 collects multi-source data during the execution of scenario simulations and real-time task guidance through platform data point embedding and recording, including: decision data, interaction data, outcome data, and feedback data; S42 uses local analysis models (such as analysis frameworks that combine rule engines and machine learning models) to process multi-source data and obtain analysis results, including: cognitive diagnosis, capability assessment and weakness identification.

[0032] In some preferred embodiments, the platform embedding and recording in step S41 may further include: By listening to front-end events, recording operation logs, and tracing API calls, we can achieve seamless collection of user interaction behavior, decision-making process, and content output. in: Decision data includes: option selection, decision time, and number of decision modifications; Interaction data includes: Q&A content with the AI ​​ethics guidance module, as well as the frequency and keywords of comments in group discussions; Results data, including students' reasoning for their decisions, discussion reports, and / or analytical papers; Feedback data includes students' self-assessment of satisfaction, self-marked difficulties, and teachers' in-class evaluations.

[0033] In some preferred embodiments, the above-mentioned S42, the local analysis model, adopts an analysis framework that combines a rule engine with a machine learning model (including but not limited to: logistic regression, decision tree, clustering algorithm); Using a local analysis model, multi-source data is processed to obtain analysis results, which may further include: Sequence pattern mining is performed on decision data, semantic analysis is performed on interaction data, structured scoring is performed on outcome data, and sentiment analysis is performed on feedback data to comprehensively derive cognitive diagnosis, ability assessment, and weakness identification results.

[0034] in: Cognitive diagnosis includes: comparing students' decisions and outcomes with the pre-set knowledge points to assess students' level of mastery; The assessment of abilities includes evaluating students’ ethical decision-making and communication skills by analyzing decision-making time, report logic, and presentation quality. Weakness identification includes: accurately identifying knowledge gaps or skill deficiencies at the individual and group levels.

[0035] In some preferred embodiments, the above-mentioned S5, which generates teaching evaluation and personalized improvement plans based on the analysis results, may further include: Construct a multi-dimensional quantitative scoring system, and based on the analysis results, comprehensively evaluate students' performance from the cognitive, ability, and attitude dimensions; Based on the analysis results of the identified weaknesses, targeted learning resources (including micro-lecture videos, interactive exercises, and extended reading materials) are automatically pushed to students. Among them, based on knowledge graphs or resource tagging systems, the weaknesses are intelligently matched with the preset learning resource library and pushed to students through the message center or task list. It provides teachers with data reports on both the class as a whole and individual students, and generates suggestions for improving teaching (including adjustments to course content, optimization of teaching strategies, and a list of students to focus on).

[0036] Based on the same inventive concept, one embodiment of the present invention also provides a segmented, progressive AI ethics scenario simulation teaching system.

[0037] Specifically, such as Figure 2As shown, the segmented, progressive AI ethics scenario simulation teaching system provided in this embodiment may include: The database construction module is used to build a progressive AI ethics scenario database based on different scenario levels and corresponding task requirements; The simulation platform construction module is used to configure the scenario simulation platform and the AI ​​ethics guidance module. The teaching task execution module, based on the AI ​​ethics scenario library, scenario simulation platform and AI ethics guidance module, performs progressive scenario simulation and real-time task guidance. The multi-source data acquisition module is used to collect and analyze multimodal teaching data in real time during the execution of scenario simulation and real-time task guidance. The evaluation and suggestion module generates teaching evaluations and personalized improvement plans based on the analysis results.

[0038] The specific implementation methods of each functional module constituting the system provided in the above embodiments of the present invention will be further described in detail below with reference to specific examples.

[0039] The database construction module builds a progressive AI ethics scenario database divided by learning stages. It includes the following units: The scenario complexity hierarchical design unit divides scenarios into the following stages according to scenario complexity: In the initial stage, such as elementary school (grades 1-6): the focus is on basic ethical judgments in everyday life scenarios. The scenarios are simple, the conflicts are clear, and the roles are relatable to students. For example, in the scenario of "Privacy Protection of AI Voice Assistants," the core conflict is "Should we ask the AI ​​for sensitive information about our family members (such as bank card numbers)?" Students are required to choose from clear options (such as "Tell the AI" or "Don't tell the AI") and explain their reasons simply. A cartoon-style interactive interface is provided, and the task duration is controlled to around 5 minutes.

[0040] Intermediate level, such as junior high school (grades 7-9): Focuses on the ethical implications of technology applications. Scenarios introduce multiple roles, multiple decision options, and fundamental technological principles. For example, the "information cocoon of AI recommendation algorithms" scenario centers on "how to deal with the impact of homogeneous content pushed by AI on one's own learning." Students can manipulate the "recommendation diversity parameter" slider to observe content changes and engage in group discussions. A simplified platform simulation interface is provided, and the task lasts approximately 8 minutes.

[0041] Advanced levels, such as high school (grades 10-12): Focus on the deep ethical dilemmas in technological development. Scenarios involve social impact, legal boundaries, and value conflicts. For example, the scenario of "AIGC (AI-generated content) copyright ownership" centers on the conflict of "attribution rights, copyright ownership, and technological integrity of AI-generated works." Students are required to write a decision report or engage in a debate, drawing on legal provisions and case studies. A case study library and report editing tools are provided, with a task time of approximately 15 minutes.

[0042] The scenario-based resource association unit matches a dedicated teaching resource package for each scenario, including but not limited to: scenario-introducing short videos, knowledge point manuals related to the curriculum standards (such as data privacy, algorithmic fairness, and intellectual property), role scripts, and decision-making reference materials. At the same time, ethical rules are embedded in the scenario logic to ensure the correctness of the teaching orientation.

[0043] The simulation platform construction module configures the scenario simulation platform and the AI ​​ethics guidance module. It includes the following units: The teaching platform function configuration unit configures the scenario simulation platform using both web and client modes, and adapts the interactive logic and interface for different learning stages, including: Primary school stage: The default is a graphical click interface, which mainly uses icons, animations and voice feedback, making it simple and intuitive to operate.

[0044] Junior high school level: Adopt a hybrid graphical and text interface, and add parameter adjustment, real-time data visualization and group text / voice discussion area.

[0045] High school level: The interface is dominated by text and document editing, and integrates case search, debate timer and structured report writing tools.

[0046] The AI ​​ethics guidance module deployment unit is based on a large language model (such as the Transformer architecture model) to build an ethics guidance engine, and is preferentially deployed on the school's local server (example configuration: Intel Xeon E3 series CPU, 64GB memory) to ensure the security of teaching data. This engine is fine-tuned and trained using massive amounts of AI ethics cases, teaching guidance corpora, and common student error samples, and possesses three core capabilities: real-time question answering, progressive questioning, and error correction.

[0047] The unit for setting guidance strategies by educational stage configures the following guidance strategies for the AI ​​ethics guidance module: Primary school stage: Adopt the "hint + encouragement" strategy, using simple and friendly language to guide and positively reinforce.

[0048] In junior high school: a "questioning + inspiration" strategy is adopted to guide students to think about the consequences of their actions and to introduce the concept of technology.

[0049] In high school: the "argumentation + reference" strategy is adopted, requiring students to cite ethical principles, legal basis or real-world cases in decision-making, so as to cultivate their rigorous critical thinking skills.

[0050] The teaching task execution module implements progressive scenario simulations and real-time guidance. It includes the following units: The scenario introduction and role assignment unit involves teachers introducing a scenario by playing videos, presenting news cases, or posing debate topics. Students are then assigned roles (mostly individual roles in elementary school, while group role assignments can be used in middle and high school).

[0051] The Decision Execution and Dynamic Interaction unit allows students to enter a simulated scenario as assigned roles, engaging in dialogue, making choices, or adjusting parameters through a platform interface. Based on the students' decisions, the platform generates and visualizes the subsequent development of the scenario in real time, creating a dynamic and coherent narrative experience.

[0052] The AI ​​assistant provides real-time guidance. During the simulation, the AI ​​guidance engine monitors the student's decision-making process and dialogue content throughout. When it detects hesitation, confusion, or clearly ethically inappropriate decisions, the engine intervenes and provides immediate guidance through pop-ups, highlighted prompts, or discussion forum messages, based on pre-set learning strategies for each grade level.

[0053] The multi-source data acquisition module collects and analyzes multimodal teaching data. It includes the following units: The multi-source data acquisition unit collects the following four types of core data through platform tracking and recording: Decision data: such as option selection, decision time, and number of decision modifications.

[0054] Interactive data: such as the content of Q&A with the AI ​​assistant, the frequency of speaking and keywords in group discussions.

[0055] Outcome data: such as student-submitted decision-making rationale, discussion reports, or analytical papers.

[0056] Feedback data includes student self-assessment of satisfaction, self-marked difficulties, and teacher's in-class evaluation.

[0057] The data intelligent analysis unit processes the collected data using a local analysis model as follows: Cognitive diagnosis: Compare students' decisions and outcomes with the pre-set knowledge points to assess their level of mastery.

[0058] Competency assessment: Evaluate their ethical decision-making and communication skills by analyzing factors such as decision-making time, report logic, and speaking quality.

[0059] Weakness identification: At the individual and group levels, accurately identify knowledge gaps or skill deficiencies.

[0060] The evaluation and suggestion module generates teaching evaluations and personalized improvement plans. It includes the following units: The multi-dimensional teaching evaluation system construction unit establishes a quantitative scoring model to comprehensively evaluate students' performance from the cognitive dimension (40 points), ability dimension (40 points), and attitude dimension (20 points), with a total score of 100 points.

[0061] The personalized improvement plan generation unit automatically pushes targeted learning resources to students based on the analysis results of their weaknesses, such as micro-lecture videos, interactive games, or extended inquiry tasks.

[0062] The Teaching Optimization Suggestion Output Unit provides teachers with an overall class learning report and individual student tutoring suggestions, clearly pointing out the strengths and weaknesses in teaching, and providing a scientific basis for subsequent course design, content focus, and teaching method adjustments.

[0063] It should be noted that the steps in the method provided by the present invention can be implemented using the corresponding components in the system. Those skilled in the art can refer to the technical solution of the system to implement the steps of the method, and can also refer to the technical solution of the method to implement the composition of the system. That is, the embodiments in the system and the embodiments in the method can be understood as preferred examples of each other, which will not be elaborated here.

[0064] The technical solution provided by the above embodiments of the present invention will be further described in detail below with reference to a specific verification example. For example... Figure 3 As shown, in this specific application example, the AI ​​ethics teaching process based on segmented learning, scenario simulation, and intelligent guidance mainly includes the following five core steps: I. Constructing a progressive AI ethics scenario database based on different learning stages As a fundamental component of the plan, the core is to design differentiated AI ethics scenarios based on the cognitive levels of students at different educational stages.

[0065] Elementary school stage (grades 1-6): The core scenario revolves around "Privacy Protection of AI Voice Assistants," focusing on a clear conflict—"Should we tell the AI ​​our family members' bank card numbers?" The interface uses a cartoon design, and students must choose between "tell the AI" and "don't tell the AI," briefly explaining their reasons. The task lasts approximately 5 minutes. Accompanying this is a short video illustrating the scenario (such as an animation demonstrating the specific consequences of AI leaking information) and a knowledge point manual (explaining the basic concepts and core meaning of "privacy protection" in a simple and easy-to-understand way).

[0066] Middle school (grades 7-9): The core scenario revolves around the "information cocoon of AI recommendation algorithms," integrating multiple roles (students, parents, platform operators) and technical operation elements. Students can drag the "recommendation diversity parameter" slider to visually observe the shift in content from "homogeneous" to "diversified," followed by group discussions. A simplified platform simulation interface (displaying real-time content change data, such as the proportion of subject-related content) and role scripts (such as "As a parent, how do you view the issue of your child being 'raised' by algorithms?") are provided.

[0067] High school level (grades 10-12): Focusing on the core scenario of "AIGC copyright ownership," this course delves into deep ethical and legal dilemmas. Students are required to write decision reports or engage in debates regarding "the right of attribution for AI-generated works," citing provisions of the Copyright Law and real-world cases (such as a copyright dispute case involving an AI painting platform). Accompanying resources include a case document library (containing typical domestic and international AIGC copyright precedents) and report editing tools (supporting structured formatting and rapid citation of legal provisions).

[0068] II. Configure the scenario simulation platform and AI ethics guidance module As the technical support component of the solution, this section is divided into two aspects: platform function configuration and AI ethics guidance module deployment.

[0069] (a) Platform Function Configuration Primary school stage: The default is a graphical click interface, with icons, animations and voice feedback as the core (for example, when a student selects "Don't tell AI", the interface will pop up a cartoon prompt "You have protected your family's privacy, great job!").

[0070] Junior high school level: Adopt a hybrid interface of graphical and text, set up a "Recommended parameter adjustment area" (supporting slider operation) and a "group discussion area" (supporting real-time text / voice communication), and visualize the data of content changes in real time (e.g. "After adjustment, the proportion of subject-related content increased from 30% to 60%").

[0071] High school level: The interface is dominated by text and document editing, and integrates "case search tool" (enter keywords to retrieve corresponding legal provisions / precedents), "debate timer" and "report template" (containing logical structure guidance, such as the argument-evidence-conclusion framework).

[0072] (II) Deployment of AI Ethics Guidance Module A large model based on the Transformer architecture is used to deploy an ethics guidance engine on a local school server (such as a server equipped with an Intel Xeon E3 series CPU and 64GB of memory). This engine is fine-tuned and trained using AI ethics cases, teaching guidance corpora, and common student error samples, enabling it to provide differentiated guidance for different educational levels. Primary school stage: Use the "hint + encouragement" strategy, and guide with simple language (e.g., "If you tell the AI ​​your bank card number, bad people might use it. Think about it carefully~").

[0073] In junior high school: Use the "questioning + inspiration" strategy to guide students to think about the consequences (e.g., "When you turn 'recommendation diversity' to the lowest level, what impact will it have on your knowledge in the long run?").

[0074] High school level: Adopt the "argumentation + reference" strategy, requiring students to cite evidence (e.g., "If you claim that 'AI-generated works belong to the creator,' please refer to Article × of the Copyright Law and the case of 'a designer suing an AI platform'").

[0075] III. Implement progressive scenario simulations and real-time guidance As the core of the program's implementation, it aims to enable students to gain a deep understanding of AI ethical issues through scenario immersion and dynamic interaction.

[0076] (I) Scenario Introduction and Role Assignment In elementary school: Teachers play animated short films (such as "Xiaoming's AI assistant leaked his mother's phone number"), and students enter the scenario as "themselves".

[0077] In junior high school: The teacher presents a news example of "a student becoming addicted to short videos due to algorithm recommendations", and students are divided into groups to play three roles: "student", "parent" and "platform".

[0078] In high school: The teacher poses the topic of "a singer claiming that AI-generated cover songs infringe on his copyright", and students are divided into groups to play three roles: "plaintiff", "defendant" and "judge".

[0079] (II) Decision Execution and Dynamic Interaction Students complete operations on the platform in the corresponding roles—clicking options in elementary school, adjusting parameters in middle school, and writing debate speeches / decision reports in high school. The platform will generate subsequent results in real time: for example, after an elementary school student selects "Tell AI", the interface will simulate the consequences of "bank card being stolen" with an animation; during a debate in high school, the platform will push a comparative analysis of the cases cited by both sides in real time.

[0080] (iii) Real-time guidance from AI assistant If students hesitate or make poor judgments during decision-making, AI will intervene immediately: in elementary school, a highlighted prompt will appear saying "Think about it again~"; in middle school, a message will be sent in the discussion area asking "Please think about: How will insufficient algorithm diversity affect your learning plan?"; and in high school, a prompt will appear when editing a report stating "Article 11 of the Copyright Law regarding the 'creative subject' can be added here".

[0081] IV. Collect and analyze multimodal teaching data As a quantitative support component of the plan, this section uses data mining to assess student learning, providing a basis for subsequent teaching optimization.

[0082] (a) Multi-source data acquisition Decision data includes: "Option selection (tell / don't tell)" and "Decision time (average 3 minutes)" in elementary school; "Parameter adjustment records (such as the number of times the slider is dragged)" in junior high school; and "Number of report modifications" in senior high school.

[0083] Interactive data includes: Q&A content between elementary school students and AI (e.g., "Why can't AI know bank card numbers?"); keywords in group discussions in junior high school (e.g., "information cocoon" and "knowledge limitation"); and the frequency of speeches and core viewpoints in debates in high school.

[0084] Results data: such as the decision-making reasons of elementary school students (e.g., "Telling AI will be used by bad people, so we don't tell it"); group summaries in junior high school (e.g., "Increasing the diversity of recommendations helps to broaden knowledge"); decision reports in high school (including legal citations, case analysis, etc.).

[0085] Feedback data includes: student self-assessment of "whether they understand privacy protection" (using a 5-point scale); and teacher in-class evaluation of "whether the group's argumentation logic is clear" (using a combination of qualitative and quantitative methods).

[0086] (II) Data Intelligent Analysis After processing the collected data, the local model outputs the following results: Cognitive assessment: For example, in primary school, "the mastery rate of privacy protection knowledge points reached 85%"; in junior high school, "the understanding rate of the concept of algorithmic fairness reached 70%"; and in senior high school, "the accuracy rate of applying copyright law provisions reached 65%".

[0087] Ability assessment: The students’ AI ethics decision-making ability and expression ability are evaluated through indicators such as “decision time (average 5 minutes in junior high school, 3 minutes in the excellent group)” and “report logic (the degree of matching of arguments and evidence in the report in high school)”.

[0088] Weaknesses include: primary school students having a vague understanding of the scope of "sensitive information"; and high school students lacking a grasp of the criteria for judging the originality of AI creations.

[0089] V. Generating Teaching Evaluation and Personalized Improvement Plans As a closed loop of the solution, this section aims to achieve accurate evaluation and teaching optimization, and promote the continuous improvement of AI ethics education.

[0090] (I) Multi-dimensional teaching evaluation system The scoring is based on three dimensions: cognition (40 points), ability (40 points), and attitude (20 points). An example is shown below: A primary school student scored 83 points in total: cognition (35 points, basic knowledge of privacy protection), ability (30 points, relatively simple expression of decision-making reasons), and attitude (18 points, active participation).

[0091] High school group: Cognition (38 points, accurate citation of legal provisions), Ability (39 points, rigorous debating logic), Attitude (20 points, full engagement), total score 97 points.

[0092] (II) Personalized Enhancement Plan For students: Learning resources are pushed to students based on their weaknesses (e.g., "Interactive games on privacy protection" for primary school students; "micro-lessons on AI-generated copyright cases" for high school students).

[0093] Teacher's side: Output class learning reports (e.g., "Junior high school students generally have a weak understanding of 'algorithmic bias' and need to supplement with 'algorithmic fairness' case studies"), and provide teaching optimization suggestions (e.g., "In high school, a 'AI ethics court' simulation session can be added").

[0094] In summary, this solution, through a complete process of "construction of scenario libraries for different learning stages - configuration of platform and AI modules - scenario simulation and guidance - data collection and analysis - evaluation and improvement," achieves grade-level adaptation, immersive interaction, personalized guidance, and scientific evaluation in AI ethics teaching, effectively solving many pain points in existing AI ethics teaching, such as "mismatch between content and learning stage, weak interactivity, and inaccurate evaluation."

[0095] The progressive AI ethics scenario simulation teaching method and system provided in the above embodiments of the present invention solves the problem of adaptability of teaching content by constructing a progressive scenario library that strictly matches the cognitive levels of primary, junior high, and senior high school students; it provides students with an immersive learning experience through multi-role, interactive, and dynamic scenario simulation, transforming abstract ethical principles into concrete decision-making practices; it provides students with real-time, personalized question-and-answer, guidance, and error correction support through a large-scale model-based AI ethics guidance module, improving teaching efficiency; it constructs a multi-dimensional evaluation system covering "cognition, ability, and attitude" by collecting and analyzing multimodal teaching data, comprehensively assessing students' ethical literacy; and it provides teachers with accurate learning diagnosis and teaching optimization suggestions through data-driven approaches, forming a closed-loop system of "teaching-evaluation-optimization".

[0096] Any matters not covered in the above embodiments of the present invention are well-known in the art.

[0097] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A segmented, progressive AI ethics scenario simulation teaching method, characterized in that, include: Based on different scenario levels and corresponding task requirements, a progressive AI ethics scenario library is constructed for different learning stages. Configure a scenario simulation platform and an AI ethics guidance module; Based on the AI ​​ethics scenario library, scenario simulation platform, and AI ethics guidance module, progressive scenario simulation and real-time task guidance are performed. During the execution of scenario simulations and real-time task guidance, multimodal teaching data is collected and analyzed in real time; Based on the analysis results, teaching evaluation and personalized improvement plans are generated.

2. The segmented, progressive AI ethics scenario simulation teaching method according to claim 1, characterized in that, The aforementioned approach, based on different scenario levels and corresponding task requirements, constructs a progressive AI ethics scenario library divided into learning stages, including: Based on the complexity of the scenarios at different learning stages, the scenarios are divided into elementary, intermediate, and advanced stages. For each graded stage, corresponding ethical scenarios and task requirements are designed. Each ethical scenario is matched with a unique teaching resource package, and ethical rules are embedded in the scenario logic. By linking ethical scenarios with ethical resources, a progressive AI ethical scenario library is constructed, divided into different learning stages.

3. The segmented, progressive AI ethics scenario simulation teaching method according to claim 1, characterized in that, The configuration scenario simulation platform and AI ethics guidance module include: It adopts a dual-mode approach of web and client, adapts the interactive logic and interface to different learning stages, and configures a scenario simulation platform; A pre-trained ethics guidance engine was built based on a large language model and deployed on a local server to ensure data security. A segmented guidance strategy is constructed for the aforementioned ethics guidance engine as an output constraint to ensure that the guidance content matches the cognitive level and teaching objectives of students at different learning stages.

4. The segmented, progressive AI ethics scenario simulation teaching method according to claim 3, characterized in that, The pre-trained ethics guidance engine based on a large language model includes: Construct training samples, including: AI ethics cases, teaching guidance corpus, and samples of common student errors; The training samples were used to fine-tune the large language model, resulting in an ethics guidance engine with real-time question answering, progressive questioning, and error correction capabilities.

5. The segmented, progressive AI ethics scenario simulation teaching method according to claim 1, characterized in that, The process of performing progressive scenario simulations and real-time task guidance based on the AI ​​ethics scenario library, scenario simulation platform, and AI ethics guidance module includes: Import an AI ethics scenario library into the scenario simulation platform and assign task roles to different learning stages; Based on the task requirements and the task roles, task decisions are made through the scenario simulation platform to form a dynamic and coherent narrative experience and obtain task simulation results. By combining the AI ​​ethics guidance module, the decision-making path and content are monitored throughout the process, and task intervention and guidance are provided in the simulation process based on the monitoring results.

6. The segmented, progressive AI ethics scenario simulation teaching method according to claim 1, characterized in that, During the execution of scenario simulation and real-time task guidance, multimodal teaching data is collected and analyzed in real time, including: By embedding and recording data on the platform, multi-source data is collected during the execution of scenario simulations and real-time task guidance, including: decision data, interaction data, outcome data, and feedback data. The multi-source data is processed using a local analysis model to obtain analysis results, including: cognitive diagnosis, ability assessment, and weakness identification.

7. The segmented, progressive AI ethics scenario simulation teaching method according to claim 6, characterized in that, The platform's embedded points and records include: By listening to front-end events, recording operation logs, and tracing API calls, we can achieve seamless collection of user interaction behavior, decision-making process, and content output. The local analysis model employs an analysis framework that combines a rule engine with a machine learning model to extract features from multi-source data and perform pattern recognition. The local analysis model is used to process the multi-source data to obtain analysis results, including: Sequence pattern mining is performed on decision data, semantic analysis is performed on interaction data, structured scoring is performed on outcome data, and sentiment analysis is performed on feedback data to comprehensively derive cognitive diagnosis, ability assessment, and weakness identification results.

8. The segmented, progressive AI ethics scenario simulation teaching method according to claim 1, characterized in that, The generation of teaching evaluation and personalized improvement plans based on the analysis results includes: Construct a multi-dimensional quantitative scoring system, and based on the analysis results, comprehensively evaluate students' performance from the cognitive, ability, and attitude dimensions; Based on the analysis results, targeted learning resources are automatically pushed to identify weaknesses. It provides teachers with data reports on both the class as a whole and individual students, and generates suggestions for improving teaching.

9. The segmented, progressive AI ethics scenario simulation teaching method according to claim 8, characterized in that, The automatic recommendation of targeted learning resources based on the weakness identification results in the analysis includes: Based on knowledge graphs or resource tagging systems, weaknesses are intelligently matched with pre-set learning resource libraries, and then pushed to students through message centers or task lists.

10. A segmented, progressive AI-based ethical scenario simulation teaching system, characterized in that, include: The database construction module is used to build a progressive AI ethics scenario database based on different scenario levels and corresponding task requirements; The simulation platform construction module is used to configure the scenario simulation platform and the AI ​​ethics guidance module. The teaching task execution module, based on the AI ​​ethics scenario library, scenario simulation platform and AI ethics guidance module, performs progressive scenario simulation and real-time task guidance. The multi-source data acquisition module is used to collect and analyze multimodal teaching data in real time during the execution of scenario simulation and real-time task guidance. The evaluation and suggestion module generates teaching evaluations and personalized improvement plans based on the analysis results.

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