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AI Literacy for Students

According to the 2023 UNESCO's "Chat GPT and Artificial Intelligence in Higher Education Quick Start Guide", the main challenges and implications of ChatGPT in higher education are:

  • Academic integrity
    • ChatGPT raises academic integrity concerns in higher education due to potential plagiarism and cheating. Reliable ChatGPT detection tools have yet to be developed.
  • Lack of regulation ChatGPT
    • ChatGPT's unregulated development raises concerns. Over 1,000 academics and leaders call for a pause to investigate risks and develop shared protocols.
  • Privacy concerns
    • In April 2023, Italy became the first country to block ChatGPT over privacy concerns and ethical issues regarding data collection and age verification, setting a precedent for AI-related data practices.
  • Cognitive bias
    • ChatGPT lacks ethical principles and can't differentiate between truth and bias or truth and fiction ("hallucination"). Critical analysis and cross-referencing with other sources are crucial when using its results.
  • Gender and diversity
    • Gender and discrimination concerns extend beyond ChatGPT to all AI forms due to underrepresentation of females in AI-related fields and generative AI's capability to perpetuate biased content and stereotypes.
  • Accessibility
    • Two main accessibility concerns for ChatGPT are restricted availability due to government regulations and uneven internet access, raising issues of equity and regional disparities in AI education and development.
  • Commercialization
    • ChatGPT offers both free and subscription options. Careful regulation is necessary for AI tools run by profit-driven companies, which may lack openness and use data for commercial purposes in higher education settings.

As the use of generative AI has increased, the effects of this technology on the environment have grown.  The full extent of generative AI's energy consumption is not completely known yet. Obtaining clear data is a challenge as the AI industry isn’t  transparent, and energy use varies wildly by model size, location, and what stage it’s in (training an AI model uses much more energy than prompting a query).

The conversation is further complicated because what data is available can sometimes be manipulated by people who are hoping to prove a certain viewpoint. Another complication is that AI is used to create energy efficiencies and in the future might significantly contribute to energy and climate innovations. How might that be factored into environmental impact?

Recommended Reading:

EPA, Greenhouse Gas Equivalencies Calculator - Calculations and References, 2024

Google, Powering a Google search, 2009

Ippolito, AI's impact on energy and water usage, 2025

Kamiya, The carbon footprint of streaming video, 2020

Lawrence, United States Data Center Energy Usage Report, 2016

Luccioni, Power Hungry Processing, 2024

Science News, Generative AI is an energy hog. Is the tech worth the environmental cost?, 2024

Tomlinson, Black, Patterson, et al., The carbon emissions of writing and illustrating are lower for AI than for humans, 2024

The intersection of artificial intelligence (AI) and intellectual property (IP) is a dynamic and complex domain involving legal, ethical, and technological considerations. AI challenges traditional IP frameworks by raising questions about authorship, ownership, and originality, particularly in contexts where AI systems generate creative works, inventions, or designs. Key areas include:

  • AI as a creator: Legal frameworks for AI-generated works and inventions
  • Training data issues: Copyright implications of using protected works to train AI models
  • Patent eligibility: How AI-related inventions fit within existing patent systems
  • Ownership questions: Who owns AI outputs - developers, users, or the AI itself?
  • International harmonization: Differences in IP protection for AI across jurisdictions
  • Trade secrets: Protection of AI algorithms and training methodologies
  • Liability frameworks: Who's responsible when AI infringes existing IP rights

Recommended Reading:

Dentons, AI and Intellectual Property Rights

Johns Hopkins University, What's Yours Isn't Mine: AI and Intellectual Property

Gaffar, H., & Albarashdi, S., Copyright Protection for AI-Generated Works: Exploring Originality and Ownership in a Digital Landscape

 

Artificial intelligence can contribute to labor exploitation by relying on low-paid, often invisible workers to perform essential tasks like data labeling and content moderation. These jobs are frequently outsourced and filled by precariously employed individuals, with little labor protection. While tech companies profit from AI's efficiency, the workers enabling it often receive minimal compensation and recognition, raising ethical concerns about fairness and equity.

Recommended Reading:

SAIS Review of International Affairs, Artificial Intelligence Colonialism: Environmental Damage, Labor Exploitation, and Human Rights Crises in the Global South

Adapted from AI Literacy and Critical Thinking, Macalester College Library.