RQ 1- What are the critical criteria that influence the effective implementation of AI for ESD in higher education?
The comprehensive analysis of criteria for integrating AI in Education for Sustainable Development (ESD) has provided valuable insights into the key factors that underpin the successful implementation of AI-driven educational initiatives. The findings reveal that Socio-Economic Inclusivity and Ethics Consideration are of utmost importance, highlighting the need to ensure equal opportunities and address ethical implications in AI adoption. Additionally, Data Privacy and Security, Faculty Development, and Technical Infrastructure are crucial considerations, emphasizing the significance of safeguarding learner data, empowering educators, and maintaining robust technological support. Moreover, Financial Sustainability emerges as a vital criteria, emphasizing the need to create financially viable and sustainable AI-driven programs. Curriculum Alignment underscores the importance of aligning AI-integrated educational content with sustainability-focused learning objectives, while Active Learning advocates for engaging learners actively through AI-based approaches. A Learner-Centered Approach emphasizes the personalization of AI-driven educational experiences, catering to individual learner needs. Furthermore, the inclusion of Sustainability Skills and Sustainability Knowledge highlights the significance of developing learners' competencies related to sustainability and equipping them to address global challenges. Finally, Access and Inclusivity stress the need for equitable access to AI-driven educational resources, ensuring that all learners can benefit from these advancements.
RQ 2- How can a robust evaluation framework be developed to assess the performance of AI integration in promoting sustainable learning outcomes?
The identified criteria for evaluating the performance of integrating AI for ESD in higher education involve interconnection of those criteria to elaborate a comprehensive and robust system that operates cohesively. This interconnected approach is imperative to guarantee the enhancement quality in Higher Education. By weaving them into a unified framework, universities can systematically evaluate the multifaceted effects of AI on teaching, allowing for a more accurate assessment of its contributions. In doing so, Higher Education can ensure that the integration of AI aligns harmoniously with their overarching quality assurance mechanisms, leading to a more effective and holistic enhancement of the teaching and learning quality they offer.
The Fig. 8 show the relevant element proposed to elaborate our framework and the relationship between them [19]
Skills:
These represent the abilities learners should develop to effectively use AI technologies and apply them to sustainability challenges in higher education. Skills include data analysis, critical thinking, problem-solving, collaboration, creativity, communication, adaptability, data literacy, digital literacy, systems thinking, interdisciplinary thinking, and a commitment to lifelong learning.
Knowledge:
This category outlines the essential knowledge learners need to possess to understand AI and its applications within the context of sustainable development. Knowledge ar-eas include AI principles, AI technologies and applications, sustainability concepts, Sustainable Development Goals, AI ethics and implications, the interconnectedness of sustainability issues, AI data literacy, AI and environmental sustainability, AI and economic sustainability, AI and social equity.
Ethics:
The ethics component emphasizes the importance of responsible AI use and ethical considerations in integrating AI for ESD. Learners should be aware of the ethical im-plications of AI, including privacy and data protection, transparency and explainabil-ity, fairness and bias mitigation, ethical decision-making, accountability in AI devel-opment, avoiding discrimination and harmful applications, inclusivity and social im-pact, ethical AI governance, integrity, openness, transparency in AI development, and respect for cultural diversity and local knowledge.
A set of learning objectives should be identified [19], the Learning Objectives must be aligned with our KSE model and focused on the twelve criteria of evaluation. We identify also the corresponding Learning outcomes to help measuring the performance. The table below shows a relevant learning outcomes and learning objectives base on UNESCO reports [20], [21], [22], [23], [24].
Table IV. Learning objectives and Learning Outcomes
Criteria
|
Learning Objective
|
Learning Outcome
|
Accessibility
|
Understand the importance of AI in ESD
|
Increased access to AI-powered resources for all learners
|
Socio-Economic Inclusivity
|
Analyze real-world sustainability challenges with AI
|
Improved enrollment and participation from underrepresented groups
|
Ethics Consideration
|
Promote ethical considerations in AI implementation
|
Ethical use of AI technologies in ESD
|
Data Privacy and Security
|
Ensure responsible d.ata handling in AI applications
|
Protection of student data and privacy
|
Faculty Development
|
Enhance faculty skills in AI integration for ESD
|
Competent faculty in AI-driven teaching
|
Technical Infrastructure
|
Explore AI technologies for sustainable solutions
|
Reliable AI-driven learning platforms
|
Financial Sustainability
|
Develop AI-based solutions for sustainable development
|
Cost-effective AI implementation for ESD
|
Curriculum Alignment
|
Integrate AI-driven activities with sustainability concepts
|
AI-integrated curriculum for ESD
|
Active Learning
|
Analyze real-world sustainability challenges with AI
|
Enhanced student engagement in active learning
|
Learner-Centered Approach
|
Foster a learner-centric environment using AI
|
Positive student experiences in personalized learning
|
Sustainability Skills
|
Apply AI tools for sustainable problem-solving
|
Improved sustainability-related skills and knowledge
|
Sustainability Knowledge
|
Develop a comprehensive understanding of sustainability
|
Enhanced understanding of sustainability concepts
|
A key component of the performance evaluation procedure when integrating AI for sustainability in higher education is continuous improvement. It is critical to have a continual improvement mindset after the initial evaluation is completed and key performance indices are integrated. This means making incremental improvements and changes to the AI-driven educational initiatives based on the evaluation outcomes. Investigating significant performance indicators and incorporating key performance indices (KPIs) are crucial for achieving this goal. In addition to supporting quality improvement, these KPIs also support the goal and vision of the organization. Studies [25], [19], [17], [14], [26] in the area of higher education have demonstrated that any enhancements in university performance and quality would be minor and temporary without an evaluation system based on KPIs. KPIs are therefore an important step in assessing university performance and encouraging improvement. They are widely acknowledged as being essential elements for improving performance and guiding decision-making in universities.
KPIs are effective instruments in the evaluation process that enable educational institutions to evaluate the extent to which learning objectives and learning outcomes are aligned. Institutions can use KPIs to identify areas for improvement and take proactive measures to close any gaps. This data-driven methodology promotes a climate of continuous improvement and provides that AI-driven education for sustainable development in higher education continues to be efficient, pertinent, and significant.
Figure 9 shows how to use effectively the KPI in the assessment of the Pedagogical consideration dimension of our framework. And Fig. 10 depicts an example of indicators in our framework