Intelligent Systems & Automation
Human-Centred AI for Research Management in Agricultural Higher Education
Study and prototype responsible decision-support methods that improve research planning, data quality and institutional learning across academia, research and extension in agricultural higher education.
- 0
- Progress
- 2
- Researchers
About this project
Agricultural universities manage interconnected academic, research and extension activities, yet decision support is often fragmented across projects, grants, publications, partnerships, finance, outreach and impact records. This project will investigate a human-centred and governance-first AI framework for institutional research management using public, synthetic or explicitly authorised data. It will map stakeholder needs, define interoperable and privacy-aware information models, test explainable analytics and retrieval-assisted decision support, and evaluate usability, fairness, accountability and data quality. The project will not copy or disclose any employer-owned source code, private institutional records or confidential implementation details.
Objectives
- Map stakeholder and information needs across academia, research and extension
- Define a privacy-aware interoperable research-information model
- Develop data-quality and provenance assessment methods
- Prototype explainable analytics and retrieval-assisted decision support
- Evaluate usability, fairness, accountability and institutional value
- Produce an open research framework, evaluation toolkit and scholarly outputs
Research priorities
- Agricultural higher-education research systems
- Human-centred and explainable AI
- Research information management and interoperability
- Data quality, provenance and responsible governance
- Decision support across academia, research and extension
- Privacy-preserving institutional analytics
Sustainable Development Goals
- SDG 2 · Zero hunger
- SDG 4 · Quality education
- SDG 9 · Industry, innovation and infrastructure
- SDG 16 · Peace, justice and strong institutions
- SDG 17 · Partnerships for the goals
Activities
- Stakeholder and literature review
- Comparative workflow and governance mapping
- Public, synthetic or authorised data-model development
- Data-quality and provenance metric design
- Explainable analytics and retrieval-assisted prototype
- Usability, fairness and governance evaluation
- Framework, toolkit and manuscript preparation
How to take part
Researchers can contribute expertise in agricultural higher education, research administration, extension, data modelling, information systems, responsible AI, HCI, evaluation or policy analysis. Institutional case material may be used only with documented permission.
Skills required
- Research methods
- information systems
- data modelling
- Python or PHP
- responsible AI
- HCI
- data governance
- academic writing
Roles needed
- Research information systems researcher
- Agricultural education specialist
- Extension specialist
- Responsible AI researcher
- HCI and evaluation researcher
- Data governance researcher
Who should apply
Open to researchers and postgraduate students in computing, agricultural education, research management, extension, information science, public administration or related fields. Access to institutional data is not required and never overrides institutional permissions.
Expected contribution
Collaborators must protect institutional confidentiality and intellectual property, use only public, synthetic or explicitly authorised data, document provenance, follow reproducible workflows, attend regular meetings and make substantive documented contributions.
