AI ETHICS FOR STUDENTS AND ACADEMIST AT UMP

Course Lecturer : Tri Hadiyanto Sasongko, S.Sos.

Group Members : 

Bilqis Humaira K (2409010023) 

Sofita Dwi Yunianti (2409010027)

FAKULTAS ILMU BUDAYA DAN KOMUNIKASI UNIVERSITAS MUHAMMADIYAH PURWOKERTO

1. Preface 

Artificial Intelligence (AI) has become an integral part of academic life from writing assignments and analyzing data to designing teaching materials. Yet, the rapid development of AI introduces ethical risks that must be addressed, such as plagiarism, personal data misuse, and manipulation through deepfakes. Therefore, this module is designed to offer a comprehensive understanding of ethical AI usage for students, lecturers, researchers, and academic staff at UMP. It covers AI definitions, opportunities in education, UNESCO–OECD ethical frameworks, Indonesian legal regulations (UU ITE, UU PDP, and Copyright Law), practical guidelines, and case studies relevant to the UMP environment. The aim is to support the UMP academic community in using AI responsibly, safely, and in alignment with academic integrity and Islamic values of progress (Islam berkemajuan).

2. Introduction to AI 

1. Definition of AI

Artificial Intelligence (AI) refers to the ability of machines to imitate human intelligence, including language understanding, learning from data, making decisions, and generating new content. John McCarthy (1956) defined AI as “the science and engineering of making intelligent machines.”

2. Brief Development Timeline

1950 – Alan Turing introduces the Turing Test.

1956 – Dartmouth Conference marks the birth of AI.

1980s–1990s – Expert systems grow.

2010s – Deep learning and big data advancements.

2022 – The rise of Generative AI (ChatGPT, Midjourney).

2024–2025 – Global debates on AI regulation; deepfake issues appear; AI expands in higher education.

C. Types of AI

1. Narrow AI (ANI) – AI designed for a specific task (e.g., Google Translate).

2. Generative AI – Creates new content such as text, images, audio, and video (e.g., ChatGPT, Midjourney).

3. Artificial General Intelligence (AGI) – Hypothetica.l AI with human-like reasoning across multiple domains.

 Generative AI is the most relevant for academic use because students use it to write essays, analyze data, and create digital designs.

3. AI in Higher Education

1. Opportunities and Benefits

1. Improved Academic Productivity

AI assists in summarizing articles, organizing references, and drafting academic texts.

2. Personalized Learning

AI helps explain difficult concepts in multiple ways. 

3. Research and Data Analytics

AI can support qualitative coding, preliminary statistical analysis, and data cleaning.

4. Creative Support

Students can generate posters, storyboards, and presentation concepts.

2. Risks

1. Hallucination – AI produces inaccurate or fabricated information.

2. Academic Plagiarism – Using AI-generated work without attribution.

3. Algorithmic Bias – AI outputs may reflect biased training data.

4. Personal Data Violations – Uploading sensitive data into public AI systems.

5. Deepfakes & Manipulation – AI-generated videos that can harm reputations.

(Example: Sri Mulyani deepfake case, August 2

3. Ethical Dilemmas

1.  Is using AI to produce the first draft of an assignment acceptable?

2. To what extent may lecturers use AI to evaluate student work?

3. Who is responsible when AI produces harmful or misleading content?

4. Ethical Framework (UNESCO & OECD)

1. UNESCO Recommendation on AI Ethics (2021)

1. Human Dignity & Rights

AI must respect human dignity.

2. Fairness & Non-Discrimination

AI should not reinforce gender, ethnic, or religious biases.

3. Transparency & Explainability

Users must know when content is AI-generated.

4. Accountability

Humans remain responsible for AI decisions.

5. Privacy & Data Protection

Data must be protected according to the principle of minimization.

6. Sustainable & Peaceful Use

AI should support constructive and peaceful purposes.

2. OECD AI Principles (2019)

1. AI must be human-centered.

2. AI systems should be robust, safe, and testable.

3. Transparency in AI processes is essential.

4. Human oversight must always be present.

5. Organizations using AI must remain ethically and legally accountable.

5. Indonesian Legal Framework

1. UU ITE (Electronic Information and Transactions Law)

Relevant areas:

Article 27 (3): prohibition of defamation → includes deepfakes that damage reputation.

Article 28 (1): prohibition of hoaxes → includes misleading AI-generated content.

Article 35: illegal manipulation of electronic information.

Example at UMP:

A student creates a deepfake of a lecturer as a joke → violates information manipulation & defamation → punishable under UU ITE + academic sanctions.

2. UU ITE (Electronic Information and Transactions Law)

Relevant areas:

Article 27 (3): prohibition of defamation → includes deepfakes that damage reputation.

Article 28 (1): prohibition of hoaxes → includes misleading AI-generated content.

Article 35: illegal manipulation of electronic information.

Example at UMP:

A student creates a deepfake of a lecturer as a joke → violates information manipulation & defamation → punishable under UU ITE + academic sanctions.

3. Copyright Law (UU No. 28/2014)

Relevant areas:

Articles 9–12: exclusive rights of creators.

Article 113: criminal sanctions for copyright violation.

Relevance to AI:

-Using AI to generate images “in the style of” a particular artist  potential infringement.

-AI-generated academic texts still require attribution to avoid plagiarism.

Example at UMP:

A student uses an AI-generated image inspired by a famous painter’s style for a campus poster → risk of violating Copyright Law.

6. Ethical Guidelines for AI Usage in UMP

1. Do’s and Don’ts for Students

Do’s

1. Use AI as an assistant, not as a replacement.
2. Always provide attribution (e.g., “Generated using ChatGPT”).
3. Verify AI-generated information using academic sources.
4. Protect privacy: do not upload personal or sensitive data.

Don’ts

1. Submitting assignments wholly written by AI without acknowledgment.
2. Generating hoaxes, misinformation, or deepfakes.
3. Using copyrighted prompts (e.g., “in the style of X”).

2. Do’s and Don’ts

Do’s

1. Use AI for rubrics, explanations, or early-stage data analysis.
2. Create assessment tasks that require critical thinking.
3. Use AI detectors cautiously (not fully reliable).

Don’ts

1. Uploading student names, NIMs, or sensitive data into public AI tools.
2. Fully delegating grading to AI.

3. Academic Writing Policy

AI output must be cited (e.g., OpenAI ChatGPT, version X.X, prompted on [date]).

Students must include an “AI Contribution Statement” explaining which parts used AI assistance.

All references generated by AI must be verified to avoid fabricated citations. 

4. Ethical Image Generation

Avoid prompts such as “in the style of [artist]” due to copyright issues.

Do not upload friends’ or lecturers’ photos to create parodies without consent.

All AI-generated images for campus use should include a label: “AI-generated asset.”

5. Safe Use of Data in Campus Research

Interview transcripts, participant identities, and confidential data must not be submitted to public AI tools.

Use local/offline AI tools for sensitive datasets.

Ensure informed consent includes digital/AI tool usage.

7. Case Studies Relevant to UMP

Case 1 — Student Uses AI to Write Assignments

Issue: Essay is 70% AI-generated without attribution.

Ethical Analysis: Violates academic honesty and integrity.

Legal Implication: Not illegal, but constitutes academic misconduct.

Solution:

-Student must revise with original critical thinking.

-AI use disclosure becomes mandatory.

Case 2 — Lecturer Uses AI to Grade Student Work

Issue: Lecturer inputs 60 essays into ChatGPT for scoring.

Ethical Analysis: AI grading may be biased or inaccurate.

Legal Implication: Privacy violation if student identities are included.

Solution:

-AI used only for early insights; final evaluation is human-based.

-Remove personal identifiers before using AI.

Case 3 — Deepfake of UMP Student

Issue: A student’s photo is used to create a deepfake video.

Ethical Analysis: Violates dignity and causes psychological harm. 

Legal Implication:

-Violates UU ITE (defamation, manipulation).

-Violates UU PDP (unauthorized use of personal data).

Solution:

-Academic sanctions + possible legal report.

-Campus-wide digital literacy education.

Case 4 — AI-Generated Campus Poster Using Copyrighted Style

Issue: Poster designed using an AI image “in the style of Van Gogh.”

Legal Implication: Potential copyright infringement.

Solution:

-Use public domain or Creative Commons–friendly prompts.

-Label content as AI-generated.

8. Visual Framework — UMP AI Ethical Decision Tree

9. Module Summary

1. AI provides major benefits but also carries risks such as plagiarism, bias, and deepfakes.

2. Ethical AI must follow UNESCO and OECD principles.

3. Indonesian laws—UU ITE, UU PDP, Copyright Law—are essential in regulating AI use.

4. Students and lecturers must uphold academic integrity in all AI-assisted work.

5. Personal data must always be safeguarded

.

6. UMP should implement a clear AI policy.

7. All AI use must prioritize verification, transparency, and responsibility.

10. References

Indonesian Laws:

UU No. 19/2016 (Amendment to UU ITE)

UU No. 27/2022 (Personal Data Protection Law)

UU No. 28/2014 (Copyright Law)

Academic Sources:

UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence.

OECD. (2019). OECD Principles on Artificial Intelligence.

         Floridi, L. (2019). Ethics of Artificial Intelligence.

Jobin, I., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines.

Mittelstadt, B. (2016). Algorithmic accountability.

News Sources:

Kompas, Tempo, Detik — coverage of 2025 Sri Mulyani deepfake case.