Madonna University
Draft Artificial Intelligence (AI) Policy
A draft framework for the responsible, transparent and ethical use of AI in teaching, research and community service.
Abstract
The development of AI policies in educational institutions is paramount for fostering a culture of responsible AI use and ensuring equitable integration across all domains. This paper outlines a comprehensive methodology for the development of AI policies tailored to the context of Madonna University, Nigeria. The methodology begins with sensitization seminars and workshops to cultivate AI literacy among staff, emphasizing ethical, legal, and social implications. A Committee, inclusive of diverse stakeholders, is then established to draft the policy, incorporating inputs from faculty and external professionals. Stakeholder engagement continues through the submission of inputs and iterative revisions, ensuring alignment with institutional objectives and concerns.
The resulting draft policy encompasses acceptable AI use, sanctions for misuse, procurement guidelines, legal implications, and responsibilities delineation. Additionally, it addresses capacity building, disclosure policies, privacy safeguards, and ethical considerations in teaching, research, and community service.
Ethical guidelines provided by the Association of African Universities, the University of Nottingham, and the Ethical Data Initiative inform the policy's ethical framework, emphasizing alignment with established standards, investment in technical capacity, and consideration of ethical implications. To mitigate ethical concerns, strategies such as transparency measures, bias detection algorithms, responsible AI design, and continuous monitoring and evaluation are proposed. These strategies aim to ensure fairness, transparency, accountability, and adherence to ethical principles throughout AI integration in higher education.
Lastly, the paper highlights the importance of leveraging AI technology for quality assurance processes in education, emphasizing efficiency, accuracy, and effectiveness in evaluating and monitoring educational standards.
Brief History of Madonna University, Nigeria
Madonna University is the pacesetter of private universities in Nigeria: the guinea pig for their establishment, at whose instance the edict governing private universities was enacted under President Ibrahim Badamasi Babangida, GCFR, and Prof. Idris Abdulkadir, then Executive Secretary of the National Universities Commission.
- 1992On 29 September 1992 the then Governor of Anambra State, His Excellency Dr. Chukwuemeka Ezeife (Okwadike Igboukwu), grants approval, which also permits the University to exist as a multi-campus university.
- 1993Founded by Very Rev. Fr. Prof. Emmanuel Mattew Paul Edeh, OFR, the University starts with the 1992/1993 academic session.
- 20 Apr 1999At a ceremony in Abuja, the Federal Government confers the Certificate of establishment and approval to operate.
- 22 May 1999The University officially opens at Okija, Anambra State, as the first private university to open in Nigeria and the first Catholic university in the West African sub-region, with Faculties of Education, Management, Social Sciences and Law.
- 20 Sep 2004At its 51st statutory meeting, the NUC approves Elele, Rivers State as the Main Campus, housing the Faculties of Sciences, Basic Medical Sciences, Health Sciences, Pharmacy and the College of Medicine.
- 1 Apr 2011The then Honourable Minister of Education (State), Hon. Barr. Olorogun Kenneth Gbagi, approves the transfer of the Faculty of Engineering to the Akpugo Campus, Enugu State.
- TodayThree campuses (Okija, Elele and Akpugo), eleven faculties and forty-three programmes, thirty-three of them postgraduate up to doctorate level. The thirty-ninth university established in Nigeria and a member of the International Federation of Catholic Universities (IFCU).
Vision
To produce graduates who can spread out to the world as Madonnites bringing peace and order; treating fellow humans as good and as worthy of honour and respect.
Mission
Our mission as a Catholic University is to provide a service-oriented, high-quality University education for the common good of African people and beyond. We seek to produce young men and women who would be decent in education and morals.
Our Motto
Our motto, “Dignitas Scientiae et Moralis” – “Decency in Education and Morals” – serves as a beacon for every member of our university community. It is a mantra that guides our intentions. It is our philosophy. It is our identity. It is who we are.
Objectives of the AI Policy
Transparent & Ethical Use
To coordinate and ensure transparency, confidentiality and ethical usage of AI in the institution.
Integration
To integrate AI in teaching, research and community services.
Safeguarding Against AI-Driven Attacks
To establish guidelines, principles and regulations for the responsible development, deployment and use of AI, in light of escalating cyberattack risks, potential loss of control, challenges in anomaly detection and monitoring, and the expansive attack surface in the digital domain.
Definition of Artificial Intelligence
Artificial Intelligence is a technology, a process or a product involving a machine (a non-human entity) that performs tasks and solves problems that would otherwise require human intelligence, or that imitates or mimics human intelligence. It is also the process of simulating human intelligence by machine, enabling it to perform human-like tasks.
Brief History of Artificial Intelligence
- AntiquityGreek myths of intelligent artificial beings.
- 1st–10th c. BCSacred mechanical objects in African and other civilizations are believed capable of intelligent actions and emotions.
- 1st c. ADHero of Alexandria creates mechanical men and other automatons, the world’s first practical programmable machines.
- 1795–1805The simplest kind of artificial neural network, the linear network, is produced.
- 1950Alan Turing publishes “Computing Machinery and Intelligence”, proposing the Turing test as a measure of machine intelligence and answering the common objections to the claim that machines can think. Isaac Asimov publishes his Three Laws of Robotics.
- 1956The Dartmouth College summer AI conference is organized by John McCarthy, Marvin Minsky, Nathan Rochester of IBM and Claude Shannon. McCarthy coins the term “artificial intelligence”.
- 1965Joseph Weizenbaum (MIT) builds ELIZA, an interactive program that holds a dialogue in English on any topic.
- 1990sRevival and emergence of machine learning (ML).
- 1994The twin robot cars VaMP and VITA-2 of Ernst Dickmanns and Daimler-Benz drive more than one thousand kilometres on a three-lane Paris highway, in heavy traffic at up to 130 km/h.
- 2000sThe genesis of generative AI.
- 2010sThe rise of AI.
- 2011–2014Apple’s Siri (2011), Google’s Google Now (2012) and Microsoft’s Cortana (2014): smartphone apps that use natural language to answer questions, make recommendations and perform actions.
- 2020sGenerative AI reaches new horizons.
- Nov 2022ChatGPT, an AI chatbot developed by OpenAI, debuts.
- 2023OpenAI’s GPT-4 is released and regarded as an impressive improvement over GPT-3.5.
- 29 Mar 2023A petition of over 1,000 signatures, including Elon Musk and Steve Wozniak, calls for a 6-month halt to what it calls “an out-of-control race” producing AI systems that their creators cannot “understand, predict, or reliably control”.
- Nov 2023The first global AI Safety Summit is held at Bletchley Park in the UK to discuss the near and far term risks of AI and the possibility of mandatory and voluntary frameworks.
Status of AI in Africa, Nigeria & Madonna
In Africa
- Traced to antiquity: African divination systems and computer science both rest on advanced mathematics.
- Significant recent growth, driven by technological advancement, increasing digitalization and a growing pool of data science and machine learning talent.
- Notable African AI scientists include Timnit Gebru, Alex Tsado and Aisha Walcott.
- 2013: Aisha Walcott and her team develop data-capture methods and analytical tools for intelligent transportation systems, the first AI infrastructure on the continent.
- 2013–2023: continued growth, with the setting up of various AI startups.
In Nigeria
- Late twentieth century: academic institutions and research organisations begin exploring AI concepts and technologies.
- Government supports the field through the National Centre for Artificial Intelligence and Robotics (NCAIR), which promotes research and development in AI and robotics.
- Academic institutions and the private sector promote AI research, education and innovation, giving rise to startups, research hubs and initiatives across the country.
At Madonna University
- The application of AI is yet to be nurtured to reality.
- The institution has laudable Information and Communication Technology infrastructure.
- Little of the AI infrastructure that is needed is yet available.
The Generative AI Concern
- Growing use of ChatGPT, Co-Pilot, Gemini and other chatbots by both students and lecturers.
- The main concern is their use to carry out assignments or to plagiarise research.
- Many universities worldwide are reviewing plagiarism policies or banning generative AI in academic programmes to protect academic integrity.
- An institutional policy is needed to weigh the benefits and risks of generative AI and guide its responsible use.
A Risk-Based Framework
Stakeholders in Africa can harness the transformative potential of AI to drive inclusive growth, innovation and development across the continent along six dimensions.
- Adopt a risk-based strategy built on non-binding regulatory tools such as sectoral guidelines or assessment checklists, while cultivating a regulatory atmosphere that encourages positive AI innovation.
- Align with national principles on human rights, democracy and the rule of law.
- Address the potential threats identified and rectify the substantive and procedural legal gaps.
- Ensure pertinence and effectiveness in comparison with existing protocols.
Infrastructure and Connectivity
Capacity Building and Education
Ethical and Regulatory Frameworks
Localization and Contextualization
Collaboration and Partnerships
Socio-Economic Impact Data Governance and Accessibility
Strengths & Weaknesses of AI Implementation
Strengths
- Takes on risk instead of humans: AI robots can do risky tasks, including in natural or man-made disasters.
- Accuracy and speed: a high degree of accuracy, less human error and faster decision making.
- Innovation: powers inventions in almost every domain and helps solve complex problems.
- Always available: operates 24/7, round the clock, without human intervention.
- Wider gains: efficiency, insight generation, scalability, cost savings, help with repetitive jobs, personalization and innovation.
Weaknesses
- Employment: replacing repetitive tasks with robots can disrupt employment standards and make people over-reliant on automation.
- Human connection: machines cannot form the bond with people that team management needs.
- Socioeconomic impact: retraining displaced workers and ensuring equitable access to opportunities is a pressing concern.
- Cost and data: high costs of creation and heavy dependence on vast amounts of data.
- Reliability and governance: reliability and robustness, human–AI collaboration, and regulatory and legal challenges.
- Ethical risks: bias, opacity and privacy are covered under Ethical Challenges below.
SWOT Analysis
Strengths, weaknesses, opportunities and threats affecting the implementation of the policy at Madonna University.
Strengths
- There is a new wave of internal democracy and quality leadership in the university
- The Proprietor and Governing council are committed to actualization of the vision and mission of the university
- Reasonable level of investment in ICT infrastructure and facilities available
- Federal Ministry of Education and National University Commission Policies and Minimum Standards
Weaknesses
- Unavailability of constant electricity
- Lack of a maintenance culture
- Weak internal revenue generation capacity
- Proprietor’s interest in revitalizing and repositioning universities
- Paucity of quality and insufficient data. This can lead to biased results or inaccurate predictions
- Internet connectivity
Opportunities
- Advancements in Information Communication Technologies (ICT)
- The new generation of prospective university candidates is computer literate
- Collaborations with Development Partners and Non-governmental organizations is practicable
- Drive for Globally Relevant Graduates
- AI-powered products and services can create new revenue streams and business opportunities
Threats
- Low level of digital skills in the field
- Paucity of experienced computer scientists and engineers
- Epileptic and inadequate funding for research and development
- Lack of a political will of staff to do what is required
- Staff attrition
- Aging Professionals
- Compliance with ethical guidelines
- Divulgement of sensitive information and operations
Methodology for Developing the AI Policy
Sensitization
Seminars and workshops on AI for staff, students and key stakeholders build AI literacy, awareness and readiness across departments. Experts are invited and hands-on sessions and real-world examples included, stressing ethical, legal and social implications (bias, privacy, transparency, accountability, job displacement). Risks that could hinder implementation are identified, and feedback and evaluation close each session.
Drafting Committee
Senate sets up a committee to draft the policy, with members from all faculties, non-teaching staff, the ICT and Legal units, and external professionals including AI-industry experts. Terms of reference and a timeframe are specified.
Call for Submissions
The Committee invites the University community and external stakeholders to submit inputs, so the policy meets the needs and concerns of all parties.
Collation
The Committee collates and synthesizes all inputs, consolidating diverse viewpoints into one cohesive draft.
Faculty Board Input
The zero draft goes to the Deans for Faculty Board input, refining the policy to each faculty’s needs and objectives.
Revision
The Committee reviews the Faculty Boards’ comments and revises the draft, so it reflects the priorities of academic units and builds ownership.
Submission to Senate
The Committee submits the draft to Senate, signalling institutional endorsement at the highest level of academic governance.
Senate Approval
Senate reviews the draft and, if satisfied, approves it, giving the policy backing across all faculties and departments.
Council Ratification
The Senate-approved copy goes to Council for ratification. This secures institutional commitment, integrates the policy into strategic planning and governance, and marks a milestone in institutionalizing AI governance in line with the University’s mission and values.
Draft Policies for AI Use in Teaching, Research, Community Service & Quality Assurance
The draft policy covers thirteen areas, with proposed limits, sanctions and responsibilities set out under each.
Acceptable Use
- Defines permissible applications of AI in staff and student activities, including academic papers, project reports and theses.
- Guidelines for AI in teaching methods and parameters for AI in research projects and publications.
- Transparent reporting of AI involvement in academic work, with full (100%) disclosure of AI content.
| Activity | Maximum AI content |
|---|---|
| Teaching | 60% |
| Community service | 40% |
| Students’ project reports | 30% |
| Academic papers | 25% |
| Research | 20% |
| Theses | 20% |
Sanctions for Misuse
- Clear delineation of violations and matching penalties, set by the degree or percentage by which the acceptable level is exceeded.
- Penalties range from rejection or repeat of the activity to disqualification, expulsion or dismissal, depending on whether a student or a staff member is involved.
- Procedures for investigation, with mechanisms for appeals and due process.
- Absolute compliance with the acceptable levels and ethical provisions is expected.
Procurement Plan
- Criteria for selecting AI technologies and vendors, with budget allocation and resource planning.
- The Vice-Chancellor sets up a procurement and tender board or committee.
- Items are advertised in at least three national dailies, screening and selection are public and transparent, and the source of funds is identified.
- Procedures to evaluate the performance and suitability of AI systems.
Legal Implications
- Compliance with university, national and international laws and regulations on AI.
- Intellectual property rights over AI-generated content, and liability and accountability frameworks for AI applications.
- Legal interpretation of any aspect of the policy is reserved exclusively for the University’s Legal Unit.
Roles & Responsibilities
Identifies the stakeholders in AI governance and assigns roles for AI development, implementation and oversight, including responsibility for data management and security, with key performance indicators for monitoring and evaluation.
| Member | Role and responsibility |
|---|---|
| Representatives of faculties | Ongoing sensitization of faculty staff, administrative matters and integration into the curriculum |
| ICT unit | Leads the implementation team and prepares the biannual report |
| Legal unit | Interpretation of ethical breaches and legal advice |
| Non-teaching staff | Secretarial services |
| Students’ representatives | Ongoing sensitization of students |
| External key stakeholders | Funding, collaboration and product utilization |
| Professionals in the AI industry | Expertise services and collaboration in research |
Responsible Data Use
- Guidelines for data collection, storage and sharing, with measures for accuracy, integrity and privacy.
- Written informed consent from the appropriate unit before data is collected, used or shared.
- Robust security measures for sensitive information, with the implementation committee lead as its custodian.
Capacity Building
- Training programmes for faculty, staff and students on AI technologies and ethics, with support for continuous learning in AI-related fields.
- Collaboration with external experts and organizations for knowledge exchange.
- Held quarterly, in batches, with pre-test and post-test outputs documented.
Full Disclosure Policy
- Transparency about the capabilities and limitations of AI systems.
- Disclosure of potential biases and risks in AI applications.
- Guidelines for informing stakeholders when AI is used in decision-making, backed by oversight and review mechanisms.
Privacy & Ethical Considerations
Principles for ethical AI development and deployment, safeguards for individuals’ privacy and dignity, and ethical frameworks for AI research and experimentation. See Ethical Considerations below.
Policy Advocacy
Engagement with policymakers, industry partners and civil society to promote responsible AI practices, and advocacy for regulations that support ethical AI development and use.
Interdisciplinary Collaboration
- Encourages collaboration across disciplines on complex AI challenges and supports interdisciplinary research projects.
- AI is integrated into the curriculum, with guidelines for synergy among faculties and international collaboration.
- Faculties meet every second semester to share best practices and challenges.
Diversity & Inclusion
Commitment to diversity in AI development teams and research participants, measures to mitigate bias and ensure fairness in AI algorithms and applications, and inclusive practices for engaging diverse communities in AI discussions and initiatives.
Monitoring & Evaluation
Frameworks for assessing the impact and effectiveness of AI policies and practices, including audits and stakeholder feedback. See Monitoring & Evaluation below.
Elements of Quality Assurance
Focus
The overall quality of educational programmes, teaching and learning processes, student outcomes, faculty qualifications and institutional resources.
Scope
Curriculum design, teaching methodologies, assessment practices, student support services and institutional governance.
Approach
A continuous process of monitoring, assessment, review and improvement throughout the academic year, built into day-to-day operations. It fosters a culture of improvement, academic excellence and accountability.
Who Carries It Out
Institutions themselves, or external quality assurance agencies that oversee higher education standards at regional or national level.
- External examiner system
- Institutional audit/visitations
- Routine monitoring
- Accreditation
- Ranking
- Post-UTME
Ethical Considerations in Integrating AI
Ethics in AI is the set of moral principles and guidelines that govern the development and use of AI to ensure responsible use: a safe, secure, humane and environmentally friendly approach. In higher education it means adhering to those principles when implementing AI in educational settings, which involves:
- Academic integrity
- Bias mitigation
- Trust
- Equity
- Privacy
- Professional development
- Informed consent
- Fairness
- Transparency
- Accountability
Alignment with Ethical Standards
Ensure AI applications in education adhere to established ethical standards.
Investment in Technical Capacity
African universities should invest in technical capacity for AI to mitigate the risk of “data colonialism”.
Ethical Implications
Experts discuss AI’s role in education and research, considering ethical implications and emerging technologies.
Teaching, Research & Community Service
In Teaching
AI tools must not exacerbate existing educational inequalities.
- Be transparent about the use of AI in teaching materials and assessments.
- Safeguard student privacy and sensitive data when using AI tools.
- Preserve the human-centric aspects of teaching and learning.
In Research
- Ensure robust data anonymization and protection protocols to safeguard individuals’ privacy.
- Proactively identify and mitigate biases to ensure fair and unbiased results.
- AI models used in research should be transparent and explainable.
- Obtain informed consent when AI is involved in research with human subjects.
- Implement algorithmic accountability, so stakeholders can understand how AI-driven decisions are made and challenge outcomes that raise ethical concern.
In Community Service
- Use diverse datasets, include human oversight in decision-making and actively monitor for bias.
- Secure AI systems and protect user data according to regulations, since community services often handle sensitive data.
- Allow human review of AI decisions.
- Use AI to augment human capabilities, not replace them, with qualified professionals available for complex situations.
- Do not let AI become a barrier to essential services for those without equal access to technology or the internet.
Ethical Challenges in Integrating AI Tools in Higher Education
Algorithmic bias
AI can inherit, perpetuate or worsen biases in its training data, leading to unfair outcomes and discrimination.
Lack of transparency
Opaque systems hinder trust, accountability and regulatory compliance.
Data privacy concerns
Reliance on vast amounts of data raises risks of breaches and unauthorized access.
- Potential misuse of AI technologies
- Repetition of ideas
- Ubiquitous nature of technology
- Resistance to change
Strategies to Mitigate Ethical Concerns
Transparency Measures
Make the inner workings, decision-making processes and data sources of AI clear to stakeholders, building trust and allowing them to judge reliability and fairness.
- Explanation techniques: interpretable models or post-hoc explanations of AI-generated decisions.
- Algorithmic transparency: disclose the algorithms, data inputs and processing steps used.
- Data transparency: explain how data is collected, stored and used, including sources, quality assessments and known biases.
- Model documentation: record design choices, assumptions and limitations.
Bias Detection Algorithms
Identify and mitigate biases that could lead to unfair or discriminatory outcomes, so AI treats different demographic groups equitably.
- Systematic assessments of bias in data, algorithms and decision-making.
- Fairness metrics, such as demographic parity or equal opportunity.
- Mitigation techniques: data preprocessing, algorithmic adjustments or fairness-aware learning.
- Regular monitoring for emerging biases, adapting strategies over time.
Responsible AI Design
Build ethical considerations and societal values in from the outset, so AI aligns with human values and serves the common good.
- Clear ethical principles for development and deployment, covering privacy, transparency, accountability and fairness.
- A focus on human needs, preferences and well-being, and on social, cultural and ethical impacts.
- Involvement of end-users, domain experts, ethicists and affected communities in design.
- Ethical risk assessment at each stage, from design to deployment, with risk mitigation measures.
Continuous Monitoring & Evaluation
Ongoing assessment of AI systems against ethical standards, regulatory requirements and organizational policies, to detect and address ethical concerns and performance issues over time.
The team, indicators and review cycle are set out under Monitoring & Evaluation below.
The integration of AI in education, exemplified by tools like ChatGPT, Google Bard, Gemini, SlidesAI and PyTorch, presents an exciting opportunity to revolutionize research, teaching and learning. However, it comes with a responsibility to address ethical considerations diligently. By prioritizing privacy, transparency, fairness, accountability and ethical use, educators can harness the power of AI to enhance the educational experience while upholding ethical standards.
Summary
Implementing the Draft Policy
Implementation brings together organizational structures, processes and resources, with effective communication and engagement with all stakeholders.
Implementation Committee
- Set up by the Vice-Chancellor, with broad membership: faculty and academic-department representatives, the ICT and Legal units, administrative and non-teaching staff, students’ representatives, external stakeholders and AI-industry professionals.
- Oversees the development and execution of the detailed implementation plans.
Implementation Plan
- A SMART plan setting out activities, timeframe, responsible staff, budget and key performance indicators for each activity.
- Covers AI integration in teaching, research and community service, enforcement of ethical guidelines, procurement of AI infrastructure and software, and capacity building.
- Awareness campaigns, workshops and training sessions explain the policy’s objectives, guidelines and implications to faculty, staff and students.
Infrastructure
- Human and technical infrastructure for successful implementation.
- Computational resources: high-performance computing clusters, GPU-accelerated servers and cloud computing.
- Specialized hardware such as GPUs and TPUs to speed up deep learning and machine learning workloads.
- Secure data storage and management for large volumes of structured and unstructured data from diverse sources.
Capacity to Build
- A cross-disciplinary AI team trained in AI technologies and applications, plus the implementation team and the Monitoring and Evaluation team.
- Interdisciplinary teamwork across computer science, mathematics, engineering, the social sciences, humanities and domain fields.
- Organizational capacity in leadership, governance and strategic planning for AI, with a culture of innovation and continuous improvement.
Monitoring & Evaluation
Monitoring and evaluation shows whether the policy is effective, where it can improve and how far it has progressed towards its objectives.
Standing M&E Team
- Constituted by the Vice-Chancellor, the team prepares the M&E schedule.
- The schedule is biannual, given the rapid and continuous change in AI technology.
- Feedback goes to Senate for continuous improvement of the policy and of AI usage.
- Draws on the biannual report of the implementation committee and on regular audits of AI systems against established standards.
Key Performance Indicators
Defined against the policy objectives, with baseline data for each to allow comparison over time:
- Percentage of faculty and staff trained in AI ethics and responsible AI practices.
- Number of AI-integrated courses and of interdisciplinary AI collaborations.
- Compliance rate with ethical guidelines in AI projects, including fairness, transparency, privacy and safety.
- Quality of the explanations AI systems provide.
- Reported incidents of AI misuse or bias, and the number of staff or students exceeding the acceptable level of AI content.
Data & Methods
- Sources: surveys, interviews, student feedback, system logs and the biannual implementation report.
- Feedback channels for users, stakeholders and affected parties to raise ethical concerns, biases or unintended consequences.
- Both qualitative and quantitative methods.
- The plan fixes when data is collected, who collects it and how it is analysed.
Review & Adaptation
- Findings guide adjustments to implementation strategies, resource allocation and policy guidelines.
- Adaptive governance: frameworks evolve with changing ethical, legal and societal norms.
- Remediation: corrective actions, policy updates or system redesigns when ethical issues or violations are found.
- The policy is reviewed every five years, with revisions kept transparent and accountable.
Proposed Funding Model
A sustainable and equitable funding model supports the hiring of skilled personnel and the infrastructure needed to enforce the policy. It combines public and private resources, international collaboration and innovation in AI governance, and international funding can also support capacity building.
Dedicated Budget Head
Allocate 5% of the institution’s annual budget to implementing the AI policy.
Industry Partners
Partner with industries interested in AI, which may provide funding, expertise or resources.
Research Agencies
Co-fund AI research projects with research agencies.
Grants & Philanthropy
Pursue external grants and engage alumni, donors and philanthropists interested in advancing AI.
Conclusion
The development of an AI draft policy takes a systematic approach to the ethical, legal, technical and societal aspects of artificial intelligence:
Stakeholder analysis
to understand concerns and expectations
Research and analysis
of existing policies and technology to inform objectives and frameworks
Public consultation
to gather feedback and ensure inclusivity
Impact assessments
of potential effects and risks on stakeholders and society
Legal and ethical reviews
to ensure alignment with laws and standards
Pilot programs
to test the feasibility and effectiveness of proposed policies
Adoption and implementation
with mechanisms established and resources allocated
Continuous evaluation
and iteration, refining the policy as technology and feedback change
Guiding AI with Decency in Education & Morals
Madonna University is committed to harnessing artificial intelligence for teaching, research and community service, with transparency, accountability and ethical use at the centre of every step.
"Rooted in faith, committed to excellence, formed for service."