Student cheating dominates talk of generative AI in higher ed, but universities and tech companies face ethical issues too – The Conversation

Generative AI in Higher Education: An Analysis of Ethical Responsibilities and Sustainable Development Goals

Advancing Quality Education (SDG 4) and Decent Work (SDG 8)

The integration of generative artificial intelligence (AI) into higher education presents significant opportunities for advancing Sustainable Development Goal 4 (Quality Education). Rather than implementing prohibitive bans, which overlook the technology’s potential, institutions have a responsibility to leverage AI to enhance learning outcomes and prepare students for future employment, in line with SDG 8 (Decent Work and Economic Growth).

• Improved Academic Achievement: Research indicates that generative AI can be a tool for improving college students’ academic performance.
• Enhanced Accessibility: The technology offers educational benefits for diverse learners, including students with disabilities, promoting more inclusive learning environments.
• Workforce Readiness: Higher education institutions are tasked with equipping students with the necessary skills for an increasingly AI-infused workplace.

Addressing Inequalities and Ensuring Inclusivity (SDG 10)

The widespread adoption of generative AI risks exacerbating existing disparities, directly challenging the objectives of SDG 10 (Reduced Inequalities). If access is not managed equitably, a significant digital divide may emerge among students.

• Economic Disparity: A gap can form between students who can afford premium AI subscriptions and those who rely on free, less-capable, and less secure versions.
• Data Privacy and Equity: Students using free AI tools often have minimal privacy guarantees, with their data being used to train commercial models. Paid versions typically offer greater data protection, creating an inequitable system where privacy is a luxury.

Institutions can address these concerns by negotiating vendor licenses that provide free, secure access for all students and include clauses that protect student data from being used for model training.

Upholding Institutional Integrity and Justice (SDG 16)

The ethical framework for AI use in academia must extend beyond student conduct to include the responsibilities of technology companies and the institutions themselves, reflecting the principles of SDG 16 (Peace, Justice and Strong Institutions).

1. Corporate Responsibility: A significant ethical conflict arises when institutions penalize students for plagiarism while partnering with technology companies that have trained their large language models on copyrighted and scraped web content without citation or consent.
2. Institutional Accountability: Colleges and universities must vet the ethical standing of AI vendors and their products before integration. Academic integrity policies require re-evaluation to reflect this new technological landscape, moving beyond a sole focus on student violations.
3. Transparency in Data Governance: When institutions enter into vendor agreements, they become owners of student interaction data. To maintain trust and accountability, the terms and conditions of these agreements must be made transparent to all community members, clarifying how data is logged, stored, and used.

Safeguarding Student Health and Well-being (SDG 3)

The use of generative AI for non-academic purposes, such as personal advice or companionship, introduces risks to student mental health, requiring institutional strategies that align with SDG 3 (Good Health and Well-being).

• Risk of Emotional Dependency: The use of AI chatbots for personal support and life advice can lead to potentially damaging emotional attachments and negative mental health outcomes.
• Mitigation Strategies: Institutions have a duty of care to mitigate these risks. Recommended actions include:
  • Formulating explicit policies that designate generative AI tools for academic purposes only.
  • Prominently promoting campus mental health services and other support resources.
  • Implementing comprehensive training for students and faculty on the responsible and safe use of AI, emphasizing the importance of personal security and privacy.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on the ethical implications of generative AI in higher education touches upon several Sustainable Development Goals (SDGs). The analysis reveals connections to the following goals:

• SDG 3: Good Health and Well-being: The article discusses the potential mental health risks associated with students using AI chatbots as companions, highlighting a tragic case of suicide. This directly relates to promoting mental health and well-being.
• SDG 4: Quality Education: This is the central theme of the article. It explores how generative AI can be integrated into curricula, improve academic achievement, assist students with disabilities, and prepare students for future workplaces, all of which are core components of providing quality education.
• SDG 10: Reduced Inequalities: The article explicitly points out that the adoption of generative AI can “exacerbate inequalities in education.” It highlights the digital divide between students who can afford paid AI subscriptions with better features and privacy, and those who must rely on free versions, thereby reducing equal opportunity.
• SDG 16: Peace, Justice and Strong Institutions: The article addresses the need for ethical governance and responsibility from both technology companies and higher education institutions. It discusses issues of data privacy, copyright infringement by tech companies, and the need for transparent and accountable policies (e.g., vendor agreements and academic integrity rules), which are fundamental to building strong and just institutions.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the issues discussed, the following specific SDG targets can be identified:

SDG 3: Good Health and Well-being

• Target 3.4: By 2030, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
  • Explanation: The article raises alarms about the risks of students forming “potentially damaging emotional attachments with chatbots,” citing a teen’s suicide linked to ChatGPT interaction. The recommendation for universities to provide “reminders about campus mental health and other resources” directly supports the promotion of mental health and well-being to prevent such tragic outcomes.

SDG 4: Quality Education

• Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurship.
  • Explanation: The article states that “higher education institutions have a responsibility to make students ready for AI-infused workplaces.” This aligns perfectly with the goal of equipping students with relevant technical skills for the modern job market.
• Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situations.
  • Explanation: The article notes that studies have shown generative AI may have educational benefits for “students with disabilities.” This connects to the target of ensuring equal access and support for vulnerable groups within the education system.

SDG 10: Reduced Inequalities

• Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regard.
  • Explanation: The article warns that if schools encourage AI use without providing free access, “there will be a divide between students who can pay for a subscription and those who use free tools.” This creates an inequality of opportunity and access to educational resources, which this target aims to eliminate.

SDG 16: Peace, Justice and Strong Institutions

• Target 16.6: Develop effective, accountable and transparent institutions at all levels.
  • Explanation: The article calls for higher education institutions to be more accountable and transparent. It argues that they should vet AI models, rethink academic integrity policies, and “prominently display the terms and conditions” of AI vendor agreements. This reflects the need for transparent institutional practices.
• Target 16.10: Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreements.
  • Explanation: The discussion on student data privacy is central to this target. The article points out that students using free tools “have few privacy guarantees” and that their data is used to train models. It advocates for licenses that “address student privacy” and protect this fundamental freedom.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

The article does not mention official SDG indicators, but it implies several practical metrics that could be used to measure progress:

Implied Indicators for SDG 3

• For Target 3.4: The number or proportion of higher education institutions that actively provide students with mental health resources and specific guidance on the safe and emotionally healthy use of AI tools. The article suggests this can be done through “reminders about campus mental health and other resources.”

Implied Indicators for SDG 4

• For Target 4.4: The proportion of university curricula that have successfully “integrated generative AI,” thereby preparing students for “AI-infused workplaces.”
• For Target 4.5: Data on the academic achievement and inclusion of students with disabilities who are provided with access to generative AI tools, as the article mentions research indicating AI “can improve college students’ academic achievement” and has benefits for these students.

Implied Indicators for SDG 10

• For Target 10.3: The percentage of students within an institution who are provided with free, equitable access to premium generative AI tools through institutional licenses. This would measure the effort to close the “divide between students who can pay for a subscription and those who use free tools.”

Implied Indicators for SDG 16

• For Target 16.6: The number of universities that have developed and published clear, transparent academic integrity and AI usage policies. The article suggests institutions should “consider changes to their academic integrity policies” and make terms and conditions public.
• For Target 16.10: The prevalence of institutional AI vendor licenses that include clauses specifying that “student data is not to be used to train or improve models,” which would serve as a direct measure of the protection of student data privacy.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: theconversation.com