Framework for a Smart Breeding 4.0 Curriculum: Insights from China and Global Implications

Abstract

This study proposes a novel curriculum framework for Smart Breeding 4.0 to address the interdisciplinary talent gap in sustainable agriculture. Responding to the limitations of traditional agricultural education, the curriculum was developed through an analysis of emerging technological trends and industry needs. It is structured around four integrated modules: (1) Foundational Theory, tracing the evolution to data-driven breeding; (2) Technology Integration, combining AI and blockchain for precision breeding; (3) Practical Innovation, using real-world platforms for simulation projects; (4) Ethics and Policy, cultivating responsibility through case studies. Teaching emphasizes project-based learning with open-source tools, while assessment combines exams, data analysis, and innovation proposals. Explicitly aligned with key UN Sustainable Development Goals (SDGs), this conceptual framework provides a foundational model for agricultural universities worldwide. The primary contribution of this paper lies in its systematic design; future research will focus on empirical validation through pilot implementation.

1. Introduction

Global agriculture is facing multiple challenges, including climate change, population growth, and resource scarcity. A study based on literature review and meta-analysis predicts that global food demand will increase by 35–56% by 2050 [1]. The Food and Agriculture Organization of the United Nations (FAO) estimates that nearly 900 million people worldwide suffer from chronic hunger. Under a baseline scenario excluding climate change, without implementing corresponding agricultural innovations, 31% of the global population (2.5 billion by 2050) will face the risk of malnutrition. When accounting for the impact of climate change, an additional 21% (1.7 billion people) could be at risk of malnutrition by 2050 [2].

To ensure sufficient food supply for a projected global population exceeding 9 billion, it is crucial to adopt smart breeding technologies. Traditional breeding techniques relying on empirical experience and natural variation selection are no longer adequate to meet the demands for efficient and precise crop improvement [3]. In this context, Smart Breeding 4.0 represents the fourth stage of the agricultural technology revolution, characterized by the deep integration of biotechnology (BT) and information technology (IT). This integration enables a paradigm shift from ‘experience-driven’ to ‘data-driven’ agriculture, with core features including genomics-driven precision design, AI-optimized genetic gain prediction, and big data-supported digital decision-making [4].

Building on this foundation, the evolution toward Smart Breeding 4.0 is characterized by the deep integration of biotechnology (BT) and information technology (IT) [3,5,6,7,8,9]. This “BT + IT” integration encompasses key technologies such as gene editing [7], multi-omics analysis [8], and AI-driven predictive modeling [9]. It is expected to significantly enhance breeding efficiency, reduce pesticide and fertilizer usage, and strengthen intellectual property protection, thereby driving transformative leaps in the agricultural seed industry. Globally, Smart Breeding 4.0 is entering a rapid development phase. For instance, Germany’s Leibniz Institute of Plant Genetics and Crop Plant Research collected genomic and phenotypic data from 12,858 wheat genotypes, using big data modeling to triple the prediction accuracy of experimental yields [10]. Blockchain technology has demonstrated significant application potential in high-throughput crop breeding, particularly in data storage, resource exchange, information retrieval, data security, and intellectual property protection [11].

However, a critical gap exists between these rapid technological advancements and the corresponding evolution of agricultural education systems. Current educational research and practice often focus on teaching individual technologies in isolation, lacking a comprehensive framework that systematically integrates BT, IT, ethical considerations, and policy dimensions. This disconnect manifests in several notable deficiencies in traditional agricultural education: (1) Disciplinary Fragmentation—where synergistic technologies like marker-assisted selection (MAS) and AI algorithms remain compartmentalized in isolated disciplinary silos, creating substantial theory–practice gaps [12]; (2) Insufficient Practical Resources—as smart breeding relies on advanced equipment and big data support that remain largely inaccessible in most universities, exacerbating unequal distribution of educational resources [13]; (3) Absence of Ethics and Policy Education—where potential risks of gene editing and policies governing cross-border germplasm sharing have not been incorporated into mainstream curricula.

Furthermore, most existing curriculum models are derived from Western contexts, with a notable scarcity of studies that leverage rapid industrial practices from other regions while maintaining a global perspective. The Smart Breeding 4.0 curriculum must therefore integrate dedicated ethics and policy modules to cultivate agricultural breeding professionals with both technical expertise and social responsibility.

This study aims to address this gap by designing and proposing a novel curriculum framework for Smart Breeding 4.0. The proposed framework is structured into four core modules: Foundational Theory, Technology Integration, Practical Innovation, and Ethics and Policy. The remainder of this paper is structured as follows: Section 2 outlines the curriculum design methodology, Section 3 details the knowledge system, Section 4 presents practical innovations, Section 5 discusses ethics and policy cases, Section 6 describes teaching and assessment methods, and Section 7 concludes with limitations and future directions.

2. Curriculum Design Research and Methods

The development of the Smart Breeding 4.0 curriculum framework was guided by a systematic approach to ensure its relevance, coherence, and educational effectiveness. This section outlines the foundational methodology employed in its design.

2.1. Foundational Design Principles

The framework is grounded in the principles of Outcome-Based Education (OBE), a student-centered paradigm that defines curriculum through exit learning outcomes [14,15]. The design process follows the “backward design” principle of the Understanding by Design framework, ensuring alignment between objectives, assessments, and learning experiences [16]. Furthermore, the curriculum aims to foster significant learning across multiple dimensions, including integration, the human dimension, and learning how to learn [17]. This principle mandates that the curriculum design begins with a clear definition of the intended learning outcomes (ILOs)—the specific knowledge, skills, and competencies that students are expected to master upon completion. These ILOs, detailed in Section 3, directly informed the selection of content, teaching activities, and assessment strategies.

2.2. Analysis of Requirements and Content Selection

The identification of core competencies and knowledge domains was achieved through a multi-faceted analysis: (a) Literature-Driven Analysis: A systematic review of recent high-impact literature in smart breeding, agricultural technology, and future skills reports was conducted. This analysis identified critical technological trends (e.g., AI-driven prediction, blockchain for IP) and salient gaps in traditional agricultural education, such as the lack of integration between BT, IT, and ethics. (b) Stakeholder Relevance: The selection of specific technologies and case studies was prioritized based on their prominence in industry discourses (e.g., FAO reports, corporate whitepapers) and their direct relevance to addressing real-world challenges like climate resilience and food security.

To ensure a rigorous and justified curriculum structure, explicit criteria were applied for content selection: (a) Technological Maturity and Industry Relevance: Technologies (e.g., CRISPR for gene editing, AI for phenotypic prediction) were included only if they have moved beyond proof-of-concept to demonstrated application in leading seed industry reports or research. (b) Alignment with Sustainable Development Goals (SDGs): Priority was given to content that directly addresses key SDGs, particularly Zero Hunger (SDG 2) and Climate Action (SDG 13). (c) Pedagogical Potential for Interdisciplinary Integration: Content was selected for its capacity to foster connections between biological, computational, and social sciences, a core aim of the framework.

2.3. Validation and Refinement

To enhance the practical validity and global relevance of the framework, its initial structure and core components were refined through discussions with a diverse group of stakeholders from both China and abroad. This process incorporated insights from international academia (e.g., the Faculty of Agriculture at Dalhousie University, Canada), national research and extension agencies (e.g., Fujian Academy of Agricultural Sciences, Fujian and Shanxi Seed Stations), industry (e.g., Fujian Nongjia Seed Industry Co., Ltd.), and grassroots agricultural practitioners (e.g., Changle Ruixiang Farm in Fuzhou). This broad consultation ensured the curriculum addresses real-world needs across the entire research-to-application spectrum, leading to refinements, particularly in strengthening the practical applicability of the Ethics and Policy module. This practical validation approach is supported by documented successes of OBE in addressing real-time agricultural challenges [18].

2.4. SDGs Alignment Methodology

The explicit integration of the United Nations Sustainable Development Goals (SDGs) into the curriculum is a central feature of this framework. The alignment was achieved through a systematic mapping process. Each intended learning outcome (ILO) defined in

Table 1. Smart Breeding 4.0 Curriculum Modules and Intended Learning Outcomes.

Module	Core Knowledge/Skills	Intended Learning Outcomes (Upon Completion, Students Will Be Able to…)	Aligned SDGs	Suggested Credit Hours
Foundational Theory	Evolution of breeding technologies (1.0 to 4.0); Digital germplasm management.	Explain the paradigm shift from experience-driven to data-driven breeding and its socio-economic drivers.	2, 9	8
Technology Integration	AI for phenotypic prediction; Blockchain for intellectual property.	Utilize a basic open-source AI tool for yield prediction; Analyze the application of blockchain in securing germplasm intellectual property.	2, 9	8
Practical Innovation	Multi-omics data integration; Project-based breeding simulation.	Design a virtual breeding scheme for improving a specific agronomic trait using public databases.	2, 9	8
Ethics and Policy	Gene editing regulations; International germplasm sharing agreements (e.g., UPOV).	Compare regulatory frameworks for gene-edited crops across different regions and draft a simplified risk–benefit assessment report.	2, 16	8

was analytically reviewed and linked to the most relevant SDG targets. For example, the outcome “design a virtual breeding scheme for improving a specific agronomic trait” was mapped to SDG Target 2.4 (implement resilient agricultural practices). This process ensured that the curriculum’s educational objectives are intrinsically tied to broader global sustainability agendas.

3. Curriculum Knowledge System Design

Smart Breeding 4.0, as the fourth stage of the agricultural technology revolution, is characterized by the deep integration of biotechnology (BT) and information technology (IT), achieving a paradigm shift from ‘experience-driven’ to ‘data-driven’ breeding. The proposed curriculum framework is structured around four integrated modules. The core knowledge points and intended learning outcomes for each module are summarized in Table 1, which serves as the foundation for the detailed design that follows.

The following subsections elaborate on the knowledge system underpinning each module outlined in Table 1.

The fundamental theories of Smart Breeding 4.0 encompass three core areas: the historical evolution of breeding technologies, the digital management of germplasm resources, and the theoretical framework for BT and IT integration. These foundational elements underpin all subsequent technological applications and practical innovations. The evolution of breeding technologies can be divided into four stages. Breeding 1.0 relied on artificial selection and natural variation, being inefficient and time-consuming [3]. Breeding 2.0 significantly improved genetic diversity through crossbreeding, yet remained heavily dependent on field trials. Breeding 3.0 introduced marker-assisted selection (MAS) and genetic engineering, markedly enhancing breeding precision [5]. Smart Breeding 4.0 integrates genomics, multi-omics analysis and artificial intelligence algorithms, achieving a paradigm shift from “experience-driven” to “data-driven” breeding [4]. Its inherently multidisciplinary nature necessitates urgent updates to outdated curriculum systems. The core of Smart Breeding 4.0’s technological integration lies in the synergy between BT (e.g., gene editing, multi-omics) and IT (e.g., AI, big data analytics), as detailed in

Table 2. Teaching Methods and Assessment System of Smart Breeding 4.0.

Teaching Method	Core Content	Assessment Metrics	Tools/Platforms
Case-Based Teaching	Case analysis based on literature and public databases	Theoretical examination (30%)	China National Crop Germplasm Bank 2.0 Project
Virtual Project-Based Learning	Simulation of real breeding scenarios	Data analysis skills (40%)	China Agricultural University’s Shennong Guxin Smart Breeding Platform
Industry-Academia Collaboration	“BT + IT” dual-mentor teaching model	Innovation proposal (30%)	The Smart Breeding Platform co-developed by the Chinese Academy of Agricultural Sciences and Alibaba
Dynamic Feedback	Tracking project iterations using open-source tools, industry expert reviews	Ethical risk assessment report	CRISPR-DT database

. This convergence enables more predictive and precise breeding decisions, accelerating the discipline’s progress. The following analysis focuses on representative technologies and their interdisciplinary integration.

3.1. Digitalization of Germplasm Resources

The teaching of this unit is designed to achieve the learning outcomes related to “Foundational Theory” in Table 1. The digitalization of germplasm resources and the construction of interoperable databases are fundamental for enabling cross-institutional data integration and comprehensive digital management. Due to the relative independence of global germplasm repositories and the vast quantity of resources, data silos are prevalent, hindering comprehensive utilization. Millions of germplasm resources have been collected and preserved, with the current critical challenge being their effective development and utilization. Genomics-based research on plant germplasm shows promising prospects in addressing this issue, particularly in the screening and utilization of core germplasm [19]. In 2023, the Chinese Academy of Agricultural Sciences and Tencent jointly launched the “National Crop Germplasm Bank 2.0 Project” (https://www.smartedu.cn/ (accessed on 5 August 2025)) with an official signing ceremony in Beijing. This database integrates hundreds of thousands of germplasm resources through digital technologies. Efficient resource management and utilization have been achieved through the construction of genomic, phenotypic, and environmental response databases. Intelligent technologies monitor the growth status and ecological adaptability of germplasm resources in in situ conservation areas, providing a scientific basis for their protection and utilization. The project aims to meet the urgent needs for digital storage, computation, visualization, and security of germplasm resources, providing digital support for 10,000 breeding institutions and 200,000 breeding researchers.

In the classroom, this theoretical knowledge will be applied through a comparative case study. Students will analyze the data architecture and access models of China’s “National Crop Germplasm Bank 2.0” alongside international platforms like the USDA’s Germplasm Resources Information Network (GRIN-Global). A guided assignment will require them to propose a strategy for integrating a specific set of germplasm data from one platform into the other, focusing on solving issues of data standardization and metadata compatibility.

3.2. Intelligent Development and Utilization of Resources

This unit focuses on developing the skills outlined under “Technology Integration” in Table 1, particularly in data analysis. The in-depth analysis of these germplasm resources using new technologies is crucial for fully unlocking their potential. For example, over 287 million single methylation polymorphisms (SMPs) have been identified in cotton, which is 100 times more than traditional single-nucleotide polymorphisms (SNPs). DNA methylation data serves as an additional resource for breeding purposes, providing opportunities to enhance and accelerate crop improvement processes [20]. Yield prediction models based on wheat phenotypic data have significantly improved prediction accuracy [21], demonstrating that resource utilization efficiency is markedly enhanced through big data integration.

To bridge theory and practice, students will complete a hands-on data analysis exercise using public datasets from sources like the Rice SNP-Seek Database or the Arabidopsis 1001 Genomes Project. Utilizing open-source tools such as R (version 4.3.1) or Python libraries (e.g., PLINK, TASSEL), they will perform a genome-wide association study (GWAS) to identify genetic markers linked to a simple trait, thereby directly practicing the “intelligent development and utilization” of genetic resources.

Advancements in multi-omics integrated analysis (genomics, phenomics, and enviromics) have been crucial for understanding complex trait genetics. The core innovation of Smart Breeding 4.0 is its theoretical framework for integrating BT and IT. This framework is exemplified by the genotype–phenotype–environment (G × E × P) interaction model, which uses AI algorithms to integrate multidimensional data and quantify environmental effects on phenotypic variation. Such data-driven approaches enhance yields and optimize metabolic pathways, demonstrating significant scientific and economic benefits.

3.3. Blockchain Technology and Intellectual Property Protection in Breeding

The content of this unit directly supports the learning outcome on blockchain applications in Table 1. The protection of intellectual property rights for germplasm resources represents a core aspect of this technology. Blockchain technology records the origin, trait data, and breeding processes of germplasm resources, ensuring their security and traceability. In germplasm resource exchanges, blockchain technology safeguards intellectual property and genetic information from tampering or misuse. We integrate blockchain into the curriculum not only for its security benefits but also to teach students how transparent, tamper-proof systems can build trust in multi-stakeholder environments like international germplasm exchange. However, educational implementation faces challenges, including the technical complexity of distributed ledgers and computational demands, which instructors must weigh against the learning objectives. For instance, in genetically modified or gene-edited crops, synthetic gene fragments that do not affect phenotypes can be embedded with intellectual property information, directly marking patent ownership at the genetic level. Several multinational seed companies have adopted this technology to protect their core varieties. Molecular marker technologies such as single-nucleotide polymorphisms (SNPs) are widely applied in crops like rice and corn to trace origins, rapidly identify parental sources of germplasm resources, and prevent counterfeit infringement.

Moving beyond conceptual understanding, students will engage in a role-playing simulation. Groups will represent different stakeholders (e.g., a multinational seed company, a public breeding institute, a smallholder farmer cooperative) and negotiate the terms of a germplasm sharing agreement. Using a simplified blockchain smart contract template (e.g., on a test network like Ethereum Ropsten), they will draft clauses that define access rights, benefit-sharing, and traceability, concretely applying blockchain for IP management.

The introduction of blockchain technology enables secure data sharing. China and neighboring countries are exploring the balance between data sovereignty and openness within the framework of seed industry cooperation. Examples include data collaboration on salt–alkali-tolerant rice between “Nanfan Silicon Valley” (a hub for agricultural breeding and biotech innovation in Hainan, China) and Southeast Asian nations, providing valuable insights for global cooperation.

3.4. Synergistic Innovation of Gene Editing and AI Algorithms

This component is central to achieving the “Technology Integration” outcomes in Table 1. Gene editing technologies achieve trait improvement through precise modification of target genes, while AI algorithms optimize the accuracy and efficiency of genotype–phenotype association predictions. For example, engineered CRISPR/Cas9 enzymes significantly improve target recognition specificity by reducing DNA cleavage rates, effectively minimizing off-target effects in gene editing [22]. AI algorithms in gene editing are not limited to target screening; they can also predict gene function and expression regulatory networks through supervised learning [23]. A flipped classroom approach will be adopted here. Students will first use AI-based prediction tools (e.g., CRISPRseek, CCTop) to design guide RNAs for a target gene, predicting off-target risks computationally. In class, the focus will shift to an ethical debate on the implications of their designs, comparing regulatory frameworks from the US (SECURE rule), the EU, and Argentina’s adaptive approach, thus integrating technical skill with policy awareness.

3.5. Multi-Component Data Integration and Model Construction

This unit provides the foundational knowledge required for the “Practical Innovation” module in Table 1. Multi-omics integrated analysis (genomics, phenomics, and enviromics) constitutes the core technological framework of Smart Breeding 4.0. For example, multi-omics analysis has demonstrated that the cloned IPA1 gene in rice extensively regulates tillering, plant height, panicle structure, and stem development. Modulating its expression can increase grains per panicle, enhance yield, improve cold/drought tolerance, and regulate stress resistance. AI algorithms play pivotal roles in this process: Convolutional Neural Networks (CNNs) analyze RGB images collected by drones to identify crop disease patches. Recurrent Neural Networks (RNNs) process time-series data (e.g., environmental temperature and humidity) to predict crop growth trends. This knowledge culminates in a capstone project design. Student teams will define a breeding objective (e.g., enhancing drought tolerance in maize). They will then outline a full multi-omics breeding strategy, specifying the genomic, phenomic, and environmental data they would collect, the AI models they would employ for integration (e.g., CNN for image analysis, RNN for time-series data), and the validation process. This project plan serves as a direct precursor to the practical simulations in Module 4. Machine learning-based metabolomics analysis has been applied in food authentication, quality assessment, personalized nutrition, and flavor analysis, further advancing precision agriculture and food industry development [24].

4. Practical Innovations in the Curriculum

The Practical Innovation module operationalizes the intended learning outcomes defined in Table 1, specifically aiming to equip students with the ability to design virtual breeding schemes. This module adopts a case-based and project-based learning approach, leveraging real-world platforms and data to bridge theory and practice. Key resources for implementation are summarized in Table 2. The practical innovation module of Smart Breeding 4.0 aims to establish a real-case-based teaching system through industry-academia collaboration, public databases, and open science resources, cultivating students’ technical application and problem-solving abilities. Table 2 presents examples of currently available public databases and industry-academia collaboration platforms for smart breeding education in Chinese universities.

The implementation of this module is designed around a semester-long, scaffolded project. Student teams of 3–4 will progress through three phases: (1) Data Acquisition and Problem Definition, where they select a crop and trait of interest from a public database; (2) Analysis and Design, where they use bioinformatics tools to analyze genetic data and draft a breeding plan; (3) Simulation and Reporting, where they present their virtual breeding program and a critical reflection on its ethical and logistical challenges. The feasibility of this approach is high, as it relies primarily on open-source tools (e.g., Galaxy, TASSEL) and public data repositories (e.g., ENSEMBL Plants, Phytozome), ensuring low cost and high accessibility for institutions worldwide. To suit varying resource levels, instructors have options ranging from user-friendly web platforms like Galaxy for routine analyses to the more advanced BreedBase system for a comprehensive experience, thereby replicating the workflows of international research consortia like the Excellence in Breeding Platform (EiB).

Public databases provide abundant resources for practical teaching in Smart Breeding 4.0. In August 2024, China Agricultural University launched the “Shennong•Guxin” Smart Breeding Platform (https://baixiao.shennong.cc/ (accessed on 12 August 2025)). This platform integrates historical agricultural expertise and multi-omics data through pre-training, combining various analytical tools and statistical models to provide theoretical support and high-precision decision-making assistance for smart breeding. Currently, the platform has collected over 10 million agricultural knowledge graph data points and more than 50 million modern agricultural production data points, covering multiple disciplines including agronomy and horticulture. The platform optimizes hybrid combination predictions using AI algorithms and employs blockchain technology for secure germplasm resource sharing, providing authentic data support for teaching.

On the “Shennong•Guxin” or similar platforms, students will not merely observe but actively complete a defined task. For example, they will be required to input phenotypic data from a provided dataset, use the built-in AI models to predict breeding values, and then select parental lines to generate a virtual progeny with optimized genetic gain for a target trait. This hands-on process mirrors the decision-making pipeline of a modern breeder.

Students can design breeding programs on the platform to enhance practical skills. Students can analyze complex trait genetics through multi-omics integration. For example, the Chinese Academy of Agricultural Sciences utilized multi-omics to reveal the high-yield mechanism of the super rice IPA1 gene. To foster a global perspective, this case study will be taught alongside the International Rice Research Institute’s (IRRI) work on the Green Super Rice (GSR) project. Students will compare the multi-omics strategies used to identify yield-related genes in the IPA1 and GSR projects, discussing how different breeding objectives (high yield vs. abiotic stress resilience) and regional environments (China vs. Southeast Asia) influence the research design and technology application. Additionally, open-source AI tools support gene function prediction and phenotype association modeling, lowering the threshold for accessing educational resources.

5. Ethics and Policy Practice Cases

The Ethics and Policy module is designed to achieve the critical learning outcomes outlined in Table 1, equipping students to navigate the complex socio-ethical dimensions of Smart Breeding 4.0. This module employs case studies and policy analysis to address the profound ethical and policy challenges arising from rapid technological development, ensuring innovation is guided by social responsibility and regulatory compliance.

This module is taught primarily through moderated debates, role-playing simulations, and structured case study analysis. To ensure robust discussion, students will be provided with a “Decision-Making Framework” handout that outlines key considerations: Technological Risk (e.g., off-target effects), Socio-Economic Impact (e.g., farmer indebtedness), Environmental Consequences (e.g., biodiversity), and Equity and Justice (e.g., access to benefits). This framework provides a consistent structure for analyzing diverse cases. The feasibility of these activities is high, as they require no specialized equipment, only curated case materials, which are widely available from sources like the FAO’s ethical guidelines and the OECD’s biotechnology regulations.

Practical innovations in Smart Breeding 4.0 must balance technological applications with ethical risks. For example, the lack of policies for cross-border germplasm resource sharing has intensified data sovereignty disputes. The curriculum design must include ethics modules that cover, for example, the UPOV convention, alongside policy case studies to cultivate students’ sense of social responsibility in technological applications. When designing breeding programs on simulation platforms, students should enhance their ethical awareness by completing specific tasks, such as implementing blockchain for secure data sharing and preparing gene editing risk assessment reports.

5.1. Ethical Challenges of Gene Editing Technology

The potential risks of gene editing technologies (e.g., CRISPR-Cas9), such as off-target effects, have sparked widespread debate. Although continuous efforts to optimize technical parameters and methods like deep learning have effectively reduced off-target rates [25], their long-term ecological impacts still require further research. The curriculum design must incorporate ethics modules that guide students in preparing gene editing risk assessment reports and analyzing potential risks and mitigation strategies through case studies. Additionally, public acceptance of gene-edited crops represents a crucial aspect of ethics education, requiring students to develop communication skills through simulated public hearings.

Students will not just discuss risks in abstract, but will apply the “Decision-Making Framework” to a specific case: the development of non-browning mushrooms using CRISPR. They will analyze this case against the backdrop of differing regulatory approaches—comparing the US (where it is not regulated as a GMO) with the EU (where it falls under GMO regulations). A graded assignment will require them to write a brief policy brief for a fictional government, recommending a regulatory path for a similar gene-edited crop in their country, justifying their decision using the framework.

5.2. Germplasm Resource Protection and Intellectual Property Management

Germplasm resources serve as the material foundation for breeding, but their cross-border sharing involves data sovereignty and intellectual property issues. However, global germplasm repositories still face intellectual property disputes in data-sharing agreements. The curriculum design must explain the International Union for the Protection of New Varieties of Plants (UPOV) and the Nagoya Protocol, analyzing the balance between data sovereignty and sharing through case studies. For example, students can design cross-border germplasm sharing plans through simulated negotiations to balance intellectual property protection with global cooperation needs.

The simulated negotiation will be a structured activity. Students will be grouped into teams representing a Global North corporation, a public agency from a Global South country (e.g., based on the Brazilian Agricultural Research Corporation—EMBRAPA model), and an international NGO. Each team will be given a set of core interests and constraints. The negotiation must produce a draft Material Transfer Agreement (MTA) that addresses issues of benefit-sharing, intellectual property rights, and future commercial use, forcing students to grapple with the real-world tensions inherent in the Nagoya Protocol.

5.3. Policy Regulations and Practical Cases

The application of Smart Breeding 4.0 technologies must comply with national policies and regulations. The curriculum design must include policy modules explaining regulatory frameworks for gene-edited crops in major countries (e.g., the United States, the European Union, and China), and analyze constraints and promotion of technology application policies through case studies (e.g., commercialization of drought-resistant transgenic corn). Additionally, students can develop holistic thinking and policy analysis skills by simulating policy-making processes to design regulatory frameworks that balance technological innovation and ecological safety.

To move beyond description, students will use a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) tool to dissect the commercialisation pathway of drought-resistant transgenic corn in a specific context, such as Kenya. They will evaluate the policy’s strengths (food security) against its weaknesses (farmer dependency on seed companies) and consider opportunities (climate adaptation) and threats (market dominance). This structured analysis cultivates a balanced and critical approach to policy evaluation.

5.4. Personal Quality Cultivation and Agricultural Responsibility

The success of Smart Breeding 4.0 depends not only on technological innovation but also on practitioners’ personal qualities and social responsibility. This course cultivates students’ integrity, teamwork skills, and agricultural responsibility through professional ethics education, legal interpretation, and practical cases. First, professional ethics education topics analyze the impact of personal qualities on industry trust and consumer safety through food safety incidents. Second, legal interpretation covers the Food Safety Law and UPOV, strengthening students’ legal awareness and compliance capabilities. Finally, simulated negotiations and program design cultivate students’ spirit of international cooperation and sense of responsibility. For instance, when preparing ethical risk assessment reports, students must balance data sovereignty and open science in germplasm resource sharing plans to reinforce social responsibility in technology applications.

The cultivation of personal responsibility will be assessed through a reflective portfolio. Students will maintain a journal throughout the module, responding to prompts after each major case study. For example, after the gene-edited mushroom debate, they might be asked to reflect on how their personal values influenced their stance and how they reconciled conflicting evidence. This shifts the assessment from purely academic knowledge to personal and professional development.

6. Teaching Methods and Assessment

The teaching and assessment strategies described in this section are the practical implementation of the framework outlined in Table 2 (Teaching Methods and Assessment System), which is designed to directly achieve the Intended Learning Outcomes defined in Table 1. The teaching methods and assessment system of Smart Breeding 4.0 aim to cultivate students’ technical application capabilities and innovative thinking through interdisciplinary collaboration, practical projects, and data-driven evaluation mechanisms.

6.1. Teaching Method Design

The teaching methods for Smart Breeding 4.0 are designed to integrate theory with practice and to develop students’ comprehensive abilities through interdisciplinary collaboration and real-case simulations. Case-based teaching using literature and public databases serves as one of the core methods. For example, students can analyze the case of the rice IPA1 gene to master technical approaches for multi-omics integrated analysis. Project-based virtual learning: This method simulates real breeding scenarios (e.g., optimizing genetic gain in drought-resistant corn breeding on the “Shennong•Guxin” platform) to develop students’ interdisciplinary collaboration skills. Students are assigned roles such as bioinformaticians, agronomists, and policy analysts to complete breeding program designs using public datasets.

To ensure the effectiveness of the project-based learning, a “Project Charter” template will be provided to each student team at the outset. This charter requires them to define their project’s scope, milestones, individual responsibilities, and conflict-resolution mechanisms upfront, fostering project management skills. Furthermore, peer instruction techniques will be used; for instance, students specializing in bioinformatics will be tasked with explaining key AI concepts to their teammates specializing in policy, thereby solidifying their own understanding and fostering true interdisciplinary communication.

6.2. Assessment System Construction

The assessment system of Smart Breeding 4.0 adopts multidimensional evaluation criteria that balance theoretical knowledge, practical skills, and innovative thinking. The system incorporates principles of continuous improvement and efficiency, aligning with the integration of lean tools in OBE systems to enhance educational quality [26]. Theoretical examination (30%): Focuses on mastering core technologies, such as principles and methods of using convolutional neural networks to analyze phenotypic data, and key concepts of digital twin technology. Data analysis skills (40%): Evaluated through public databases and open-source tools. Students are required to complete comprehensive multi-omics analysis reports to assess their ability to predict gene functions and model phenotype associations. Innovation proposals (30%): Students must design climate-resilient breeding programs incorporating cited literature cases and ethical risk assessments, cultivating their ability to solve complex problems.

The “Innovation Proposal,” a key component of the assessment, will be evaluated using a detailed rubric to ensure objectivity and transparency. The rubric outlines criteria and performance levels across four dimensions:

Technical Soundness (40%): Excellent (90–100%): Proposal is based on a robust literature review and employs technically feasible, cutting-edge methods. Competent (70–89%): Methods are sound but may lack innovation or some detail. Developing (<70%): Methods are unclear or technically flawed.

Data Analysis and Interpretation (30%): Excellent: Data is leveraged creatively, and analysis directly and convincingly supports the breeding strategy. Competent: Data analysis is correct but may be superficial or lack depth. Developing: Analysis is incorrect or irrelevant.

Ethical and Policy Consideration (20%): Excellent: Proposal thoughtfully addresses potential ethical, social, and regulatory hurdles, proposing realistic mitigation strategies. Competent: Identifies major issues but mitigation is vague. Developing: Fails to address key ethical/policy dimensions.

Clarity, Innovation and Feasibility (10%): Excellent: Proposal is exceptionally well-written, highly innovative, and clearly feasible within constraints. Competent: Proposal is clear and feasible but conventional. Developing: Unclear or entirely unfeasible.

This rubric will be provided to students at the beginning of the course to clearly communicate expectations.

6.3. Value of Sustainable Education

The educational practice of Smart Breeding 4.0 represents not merely technological dissemination, but also serves as a strategic pillar for global food security and ecological balance, ideally aligning with the United Nations SDGs (Figure 1). Alignment with UN Sustainable Development Goals (SDGs): The curriculum explicitly addresses SDGs. For instance, Developing disease-resistant varieties can reduce pesticide dependence while improving crop yield and quality, supporting SDG 2 (Zero Hunger). Breeding abiotic stress-tolerant crops enables adaptation to varying degrees of climate change, aligning with SDG 13 (Climate Action). Open science practices: Sharing germplasm data through public databases and open-source tools lowers barriers to educational resources, promotes educational equity, and indirectly supports SDG 4 (Quality Education).

The connection to SDGs will not be implicit but will be explicitly assessed. In their innovation proposals, students will be required to map their project’s objectives and potential outcomes to at least two specific SDG targets (e.g., SDG 2.4, 13.1), justifying the linkage in a dedicated section of their report. This ensures that sustainability is actively considered and evaluated.

6.4. Dynamic Feedback Mechanism for Personal Competencies

The cultivation of personal abilities requires continuous tracking and optimization through dynamic feedback mechanisms. The course incorporates professional ethics and agricultural responsibility into its evaluation framework, specifically assessing students’ ethical awareness and relevant legal knowledge. Practical evaluation occurs through team project implementation. For example, in drought-resistant crop design projects, students’ collaboration and organizational skills are evaluated. Peer reviews and mentor feedback provide improvement suggestions. Students must work in teams to complete tasks ranging from gene editing target screening to experimental design, with mentors providing targeted feedback based on their performance. Additionally, students need to integrate ethical and social responsibility considerations into their project designs to evaluate their comprehensive capabilities. For instance, they must analyze potential risks (e.g., off-target effects) in disease-resistant crop designs and propose risk mitigation strategies. In summary, the multidimensional evaluation and dynamic feedback mechanism facilitate holistic improvement in students’ personal competencies.

The feedback mechanism will be structured. After key milestones, students will participate in a “360-degree feedback” session: they will receive written feedback from the instructor (based on the rubric), undergo a peer assessment within their team regarding collaboration, and complete a self-assessment reflecting on their contribution and learning. This multi-source feedback provides a comprehensive view of their personal competency development, which will be discussed in a brief mid-term mentoring meeting with the instructor.

7. Conclusions

This study proposes a comprehensive curriculum framework for Smart Breeding 4.0, directly addressing the identified gap between rapid technological advancement and stagnant agricultural education models. The primary contribution of this work is the systematic integration of biotechnology (BT), information technology (IT), ethics, and policy into a coherent four-module structure (Foundational Theory, Technology Integration, Practical Innovation, and Ethics and Policy), explicitly aligned with the United Nations Sustainable Development Goals (SDGs). The framework moves beyond theoretical discussion by providing detailed implementation strategies, including specific teaching methods, case-based activities, and a transparent assessment system with clear rubrics, thereby offering a practical and actionable model for agricultural universities (

Table 3. Module Design of the Smart Breeding 4.0 Curriculum System.

Module Name	Core Content	Key Technologies/Tools	Case Studies/Application Scenarios
Foundational Theory	Evolution of breeding technologies, digitization of germplasm resources, BT and IT integration	CRISPR-Cas and multi-omics integration analysis	Rice IPA1 gene analysis
Technology Integration	Synergy of gene editing and AI algorithms, multi-omics data integration, big data and blockchain-driven technologies	“Shennong•Guxin” Intelligent Breeding Platform	Yield prediction of hybrid combinations based on multi-omics data integration
Practical Innovation	Industry-academia collaboration platforms, public database applications, ethics and policy practices	National Crop Germplasm Bank 2.0 and CRISPR-DT database	Drought-resistant maize design
Ethics and Policy	Gene editing ethics, germplasm resource protection and cross-border sharing, personal quality cultivation	International Union for the Protection of New Varieties of Plants and blockchain technology	“Belt and Road” seed industry cooperation framework

).

However, this research, as a conceptual design study, has inherent limitations. The most significant limitation is that the framework’s educational effectiveness and practical feasibility have not yet been empirically validated through actual teaching implementation. The proposed benefits for student learning outcomes and interdisciplinary skill development remain hypothetical until tested in a real classroom setting.

Therefore, the critical next step is a pilot implementation and rigorous evaluation. Future research will involve delivering this curriculum as an elective course or a summer module in partnership with one or more agricultural universities. The evaluation will employ a mixed-methods approach to gather robust evidence: (1) Quantitative pre- and post-course surveys using validated scales (e.g., the Interdisciplinary Cognitive Scale) to measure changes in students’ interdisciplinary thinking abilities; (2) Qualitative analysis of students’ project reports and reflective portfolios using the provided assessment rubrics to evaluate technical proficiency, ethical reasoning, and personal competency development; (3) Focus group interviews with students and instructors to gather in-depth feedback on the learning experience, curriculum challenges, and areas for improvement. This empirical phase is essential for refining the framework and demonstrating its tangible impact, ultimately contributing to the transformation of agricultural education for global sustainability.

The proposed curriculum framework, detailed in its modules, teaching activities, and assessment criteria, serves as a foundational model and a call to action for educators and policymakers. It underscores the urgent need to modernize agricultural education by embracing an interdisciplinary, ethically grounded, and practice-oriented approach to cultivate the talent required to meet the challenges of the 21st century.