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Digital Literacies (Reguler)
Lecture: Tri Hadiyanto Sasongko S.Sos
29/11/2025
Preface
Artificial Intelligence (AI) has become an essential part of modern academic life. Students,
lecturers, researchers, and academic staff now use AI for writing assistance, translation, data
analysis, and the creation of digital learning materials. Because AI can generate content,
make decisions, and process personal information, it is important for the academic
community to understand how to use it responsibly and ethically.
At Universitas Muhammadiyah Purwokerto (UMP), AI ethics is especially relevant as
academic tasks increasingly rely on AI tools. Without proper guidance, AI use can lead to
plagiarism, misinformation, biased results, and violations of privacy or copyright. This
module provides a clear overview of AI fundamentals, its historical development, and the
opportunities and risks associated with its use. It also integrates Indonesian legal frameworks
UU ITE, UU PDP, and UU Hak Cipta, that regulate digital behavior, data protection, and the
use of creative works.
By referring to global standards such as the UNESCO and OECD AI Principles, this module
aims to help UMP’s academic community use AI ethically, transparently, and in alignment
with academic integrity. Through this understanding, students and educators can benefit from
AI while ensuring fairness, safety, and respect for human values.
Introduction to AI
- Definition of AI:
Artificial Intelligence (AI) is a field of computer science that focuses on creating
systems capable of performing tasks that normally require human intelligence.
According to John McCarthy (1956), AI is the science and engineering of making
intelligent machines. Russell and Norvig (2010) describe AI as the study of intelligent
agents that can perceive their environment, process information, and act toward
specific goals. AI enables machines to learn from data, understand language,
recognize patterns, make decisions, and even generate new content. - Timeline
AI development has progressed through several key stages:
● 1950: Alan Turing introduces the Turing Test, questioning whether machines
can “think.”
● 1956: Dartmouth Conference officially establishes AI as an academic field.
● 1970s–1980s: AI Winter due to lack of technological progress.
● 1997: IBM’s Deep Blue defeats world chess champion Garry Kasparov.
● 2010–2015: Deep Learning and Big Data accelerate AI research.
● 2022–present: Generative AI revolution (ChatGPT, MidJourney, ElevenLabs)
makes AI widely accessible .
The era from 2022 onward marks AI’s mainstream adoption, especially in education
and content creation. - Types of AI
There are several main categories of AI:
a. Narrow AI (Weak AI)
AI designed to perform only one specific task.
Examples: Recommendation systems on e-commerce
This type of AI has no consciousness and works only based on training data.
b. Generative AI
A type of AI capable of producing new content,
Such as: audio or video
Generative AI uses large machine learning models (Large Language
Models/LLMs) to produce human-like content.
c. Artificial General Intelligence (AGI)
A conceptual AI that has intelligence equal to or surpassing humans across
various domains.
AGI does not yet exist, but it is the long-term direction of research.
AI in Higher Education
- Opportunities
a. Productivity
AI tools help students and academic staff complete tasks more efficiently.
Generative AI can produce draft essays, summaries, presentation slides, and
visual designs within minutes. For lecturers and researchers, AI can assist with
grading support, data organization, scheduling, and creating teaching
materials. This increased productivity allows individuals to focus more on
critical thinking, deeper learning, and innovation.
b. Personalized Learning
AI enables individualized learning experiences by adapting materials to
students’ abilities and needs. Language-learning platforms, AI tutors, and
personalized feedback systems help students understand complex concepts at
their own pace. For example, tools like Khan Academy with GPT or
Ruangguru AI can adjust explanations, examples, and exercises to each
student’s learning style.
c. Research Support
AI enhances research efficiency through data analysis, literature review
assistance, and simulation modeling. Machine-learning tools can detect
patterns in large datasets, while AI-powered search engines help researchers
find relevant articles faster. Generative AI can also assist in designing research
instruments, summarizing findings, or visualizing results. - Risks
a. Hallucinations
AI systems may produce incorrect, misleading, or fabricated information,
known as hallucinations. Students who rely on AI without verification risk
using false facts, incorrect references, or misleading explanations in their
academic work.
b. Plagiarism
AI-generated text may resemble existing works or contain unoriginal ideas.
Students who copy AI output directly may unintentionally engage in
plagiarism or violate academic integrity. Overreliance on AI can also
undermine students’ critical thinking and writing skills.
c. Deepfakes
AI can create realistic fake videos, audio recordings, or images. In a university
setting, deepfakes targeting lecturers, administrators, or student leaders can
damage reputations and create confusion. Sharing such content may also
violate digital ethics and national regulations.
d. Bias
AI systems learn from data that may contain cultural, gender-based, or
linguistic bias. This can lead to unfair recommendations or inaccurate
assessments. For example, AI-based evaluation tools might favor certain
writing styles or languages, disadvantaging some students.
- Ethical Delimmas
a. What Counts as “Acceptable Assistance”?
Students may use AI for brainstorming or language correction, but using AI to
write entire assignments raises ethical problems. The dilemma lies in
determining how much AI involvement is still compatible with academic
honesty.
b. Data Privacy vs. Learning Efficiency
Lecturers may wish to use AI to analyze student performance or create
personalized feedback. However, uploading student grades or personal
information into public AI platforms risks violating privacy laws and ethical
guidelines.
c. Creativity vs. Ownership
AI-generated content raises questions about authorship. If a student uses AI to
create an essay or design, who owns the final product? Does the student need
to disclose AI usage? These dilemmas require clear university policies.
d. Accuracy vs. Convenience
AI-generated materials are fast and convenient, but not always reliable.
Students and lecturers must choose between speed and accuracy, especially
when academic quality and integrity are at stake.
AI in higher education offers transformative benefits but must be approached with awareness
of its risks, limitations, and ethical consequences. Universities like UMP must guide students
and staff to use AI responsibly, ensuring that technology enhances learning without
compromising integrity, fairness, or human judgment.
Ethical Framework (UNESCO, OECD)
- UNESCO
● Human Rights & Human Dignity: AI must respect human rights and dignity.
● Fairness & Non-discrimination: AI must not cause discrimination based on
gender, race, religion, or language.
● Transparency & Explainability: AI use must be transparent; users have the
right to know if they are interacting with AI.
● Accountability & Responsibility: creators and users of AI are responsible for
its impact.
● Privacy & Data Protection: personal data must be protected.
● Sustainability: AI should support education, social sustainability, and
development.
● Peaceful Use: AI must not be used for violence, weapons, or harmful
activities. - OECD
● Inclusive Growth & Well-being: AI must benefit society.
● Human-centered Values: technology must respect human values.
● Transparency: AI processes must be explainable.
● Robustness, Security & Safety: AI must be safe, accurate, and minimize
errors.
● Accountability: AI users, institutions, and developers are responsible for its
use.
Indonesian Legal Framework
The responsible use of Artificial Intelligence in academic environments must follow national
regulations that govern electronic communication, personal data protection, and copyright. In
Indonesia, three key legal frameworks are essential for guiding ethical and lawful AI
practices in higher education settings:
- UU ITE (Undang-Undang Informasi dan Transaksi Elektronik)
- UU PDP (Undang-Undang Perlindungan Data Pribadi)
- UU Hak Cipta (Copyright Law)
These laws help ensure that AI use in universities remains safe, respectful, and compliant
with national standards. - UU ITE (Electronic Information and Transactions Law)
The UU ITE regulates online behavior, including the creation, distribution, and use of
digital information. In the context of AI, the law becomes important when dealing
with hoaxes, manipulated content, defamation, and digital harm.
a. Hoaxes
AI tools especially generative AI can create false information such as
fabricated news, incorrect academic claims, or misleading explanations.
Under UU ITE, spreading hoaxes that cause public harm is a violation, even if
the content is AI-generated.
Example: A student uses AI to create a fake announcement stating that UMP
classes are cancelled for a week, then shares it on social media. This act can be
categorized as spreading false information under UU ITE.
b. Manipulation
AI can manipulate audio, images, or videos to create deepfakes that appear
real. UU ITE prohibits the distribution of manipulated content that can mislead
the public or harm reputation.
Example: A deepfake video edits a lecturer’s speech to make it look like they
made offensive remarks. Sharing or creating such content can lead to legal
consequences.
c. Defamation and Digital Harm
Using AI to generate insulting statements, false allegations, or harmful
narratives about an individual can be considered defamation under UU ITE.
Digital harm also includes actions that damage someone’s reputation or sense
of safety through AI-generated content.
Example: Using ChatGPT to generate a fake accusation against a student
leader and posting it online constitutes digital harm and potential defamation. - UU PDP (Personal Data Protection Law)
The UU PDP regulates how personal data is collected, stored, processed, and shared.
Since AI systems often use large datasets, including personal information,
understanding this law is essential.
a. Collection and Storage of Personal Data
Personal data includes names, student ID numbers (NIM), photos, voice
recordings, addresses, and academic records.
Uploading this information into AI systems may violate UU PDP unless there
is explicit consent and secure processing.
Example: A lecturer uploads students’ grades into a public AI tool to generate
feedback comments. This violates UU PDP because grades are protected
personal data.
b. Privacy in AI Training
AI models may be trained on massive datasets that include personal
information. Using personal data without permission for training AI systems is
illegal.
Example: A project uses voice recordings of UMP students to train a
speech-recognition model without informing them. This violates UU PDP’s
data processing requirements.
c. Consent Requirements
Any collection, processing, or sharing of personal data must be based on clear,
informed consent.
This includes ensuring that users understand how their data will be used,
stored, and protected.
Example: A research team asks students to upload face photos for an AI
experiment. They must provide written consent explaining:
■ how photos will be processed
■ how long data will be stored
■ whether data will be shared
■ how students can withdraw consent
Failing to provide this violates UU PDP.
- UU Hak Cipta (Indonesian Copyright Law)
UU Hak Cipta protects original creative works, including academic writing, images,
books, music, software, and digital designs. AI raises new challenges related to
ownership, datasets, and attribution.
a. Ownership of AI Outputs
AI-generated content does not remove the user’s responsibility for originality.
While Indonesian law does not fully define authorship of AI-created works,
the user who submits the prompt is responsible for ensuring the output does
not violate copyright.
Example: A student uses an AI tool to generate a poster for a UMP event.
Even though AI created the image, the student must ensure it does not copy a
copyrighted design.
b. Datasets Trained on Copyrighted Works
Generative AI models are often trained on large datasets that may include
copyrighted books, images, or artworks.
Using AI outputs that closely resemble copyrighted materials can lead to
copyright infringement.
Example: An AI tool generates an illustration that closely imitates a
well-known artist’s work. Using this in a thesis or university publication may
violate UU Hak Cipta.
c. Referencing and Attribution
When using AI to support academic writing, users must clearly indicate how
AI contributed to the work.
Additionally, if AI provides factual information or summarizes copyrighted
texts, proper citations must still be included.
Example: A student uses ChatGPT to summarize a journal article. The
summary must include:
■ citation of the original article
■ acknowledgment of AI assistance (if required by campus policy)
Ethical Guidelines for AI Usage in UMP
To ensure responsible and ethical use of Artificial Intelligence within Universitas
Muhammadiyah Purwokerto (UMP), clear guidelines are needed for students, lecturers,
researchers, and academic staff. These guidelines help maintain academic integrity, protect
personal data, and align AI practices with Indonesian legal frameworks and global ethical
standards.
- Do’s & Don’ts for Students
a. Do’s:
■ Use AI as a support tool, not as a full replacement for learning, writing,
or critical thinking.
■ Verify all AI-generated information by cross-checking with credible
sources.
■ Acknowledge AI assistance when required by the assignment or
lecturer.
■ Use AI for brainstorming, summarizing texts, language correction, or
generating ideas.
■ Protect personal data by avoiding the upload of NIM, photos, voice
notes, academic records, or sensitive information into AI tools.
■ Follow copyright rules when using AI for images, posters, or academic
visuals.
b. Don’ts:
■ Do not submit AI-generated content as your own work without
modification, analysis, or attribution.
■ Do not rely on AI for factual references without checking accuracy (AI
hallucinations).
■ Do not use AI to create manipulative or harmful content, including
deepfakes or misleading messages (violates UU ITE).
■ Do not upload confidential documents, exam questions, or research
data into AI platforms.
■ Do not use copyrighted characters, artworks, or academic texts without
permission when generating AI visuals. - Do’s & Don’ts for Lecturers
a. Do’s:
■ Guide students on acceptable AI usage for each assignment.
■ Use AI for planning, brainstorming, rubric creation, or early drafts, but
always review and adjust output manually.
■ Protect student privacy by keeping academic records offline and away
from AI tools.
■ Apply transparency when AI is used in grading or content creation.
■ Use AI ethically for instructional materials, ensuring accuracy and
avoiding biased content.
b. Don’ts:
■ Do not upload student grades, attendance records, or personal
information into AI tools (violates UU PDP).
■ Do not allow AI to grade fully automatically without human review.
■ Do not use AI-generated content without verifying facts and ensuring
alignment with course learning outcomes.
■ Do not rely solely on AI to detect plagiarism, as false positives may
occur.
- Academic Writing Policy
To maintain academic integrity, UMP should implement the following guidelines for
AI-assisted writing:
a. AI may be used for:
■ Brainstorming ideas
■ Outlining drafts
■ Language improvement and grammar support
■ Explaining difficult concepts
■ Summarizing or paraphrasing, with proper verification
b. AI must NOT be used for:
■ Writing full assignments, essays, or reports
■ Generating fabricated citations or references
■ Replacing original analysis and argumentation
■ Creating research data or fake results
c. Students must ensure:
■ All arguments and conclusions reflect their own thinking
■ AI-generated text is reviewed, edited, and rewritten
■ All sources (including AI) are properly cited according to UMP’s
citation rules
Using AI dishonestly is considered academic misconduct.
- Use of AI in Research
AI can support research, but ethical safeguards are required:
a. Allowed Uses
■ Data cleaning, classification, or pattern detection
■ Assistance in literature review (with verification)
■ Drafting research instruments (questionnaires, interview guides)
■ Statistical analysis and modeling using AI-powered tools
■ Visualizations generated through trusted platforms
b. Prohibited Uses
■ Generating fake data, simulated participants, or fabricated results
■ Uploading raw data containing personal information into public AI
tools
■ Using copyrighted datasets without permission
■ Training AI models using participant data without consent
■ Letting AI write the full methodology, findings, or discussion sections
c. Ethical Requirements
■ Researchers must clearly explain what role AI played in the research
process.
■ Informed consent is required if participant data will be processed by
AI.
■ All AI-driven analysis must be transparent, reproducible, and properly
documented.
- Citation and Attribution Rules
When AI is used in academic writing, UMP requires clear acknowledgment.
a. How to Cite AI Text-Generation Tools
Example using APA: OpenAI. (2025). ChatGPT (Version 5.1) [Large language
model]. https://chat.openai.com/
In-text citation: (OpenAI, 2025)
b. Attribution Statement
Students should include a note such as:
“Part of this assignment involved using AI (ChatGPT) to assist in
summarizing and refining language. All ideas and final content were reviewed
and written by the author.”
c. Rules for Attribution
■ Cite AI when it contributes directly to your writing.
■ Do not cite AI as a source of factual information—verify with books,
journals, or credible sources.
■ Cite original authors when AI summarizes or paraphrases their works. - Safe Use of Images and Data
a. Image Safety Guidelines
■ Use AI-generated images only if they do not copy or closely imitate
copyrighted works.
■ Avoid using images that resemble real people without consent.
■ For university events, ensure AI visuals respect UMP’s branding and
identity.
b. Data Safety Guidelines
■ Do not upload personal data (NIM, phone number, photos, voice
recordings).
■ Do not upload internal documents (course materials, exam questions,
student records).
■ Use anonymized data when testing AI systems.
■ Follow UU PDP guidelines when collecting and storing research data.
c. Examples
■ Safe: Using Canva AI to generate an original poster for a campus
event.
■ Not Safe: Uploading scanned student KTPs into an AI to autofill a
form.
■ Not Safe: Using AI to generate a photo of a “UMP student” using a
real person’s face without permission.
Module Summary
- AI is widely used in academic environments, and understanding its definitions,
capabilities, and limitations is essential for students, lecturers, and researchers at
UMP. - AI in higher education provides major benefits, including improved productivity,
personalized learning, administrative support, and enhanced research processes, but
these benefits must be balanced with academic integrity. - AI also introduces significant risks, such as misinformation (hallucinations),
plagiarism, deepfakes, algorithmic bias, and potential misuse that may harm
individuals or disrupt campus activities. - Global ethical frameworks like UNESCO and OECD emphasize human-centered AI,
fairness, transparency, accountability, and data security, principles that guide ethical
AI adoption in the UMP academic community. - Indonesian legal regulations, UU ITE, UU PDP, and UU Hak Cipta, are crucial to
ensuring that AI usage at UMP complies with national standards regarding hoaxes,
data protection, consent, and copyright. - UMP specific ethical guidelines define acceptable and unacceptable AI practices,
including do’s and don’ts for students and lecturers, academic writing standards,
proper attribution, and safe handling of images and personal data. - Responsible AI literacy is a core academic skill, helping the UMP community use AI
effectively while upholding ethics, protecting privacy, preventing misuse, and
maintaining the trustworthiness of academic work.
References
- Indonesian Legal Frameworks
Pemerintah Republik Indonesia. (2008). Undang-Undang Nomor 11 Tahun 2008
tentang Informasi dan Transaksi Elektronik (UU ITE) beserta perubahannya.
Pemerintah Republik Indonesia. (2022). Undang-Undang Nomor 27 Tahun 2022
tentang Perlindungan Data Pribadi (UU PDP).
Pemerintah Republik Indonesia. (2014). Undang-Undang Nomor 28 Tahun 2014
tentang Hak Cipta (UU Hak Cipta). - Academic Sources
OECD. (2019). OECD principles on artificial intelligence. OECD Publishing.
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO
Publishing.
Sasongko, T. H. (2024). Materi #3 – Etika Penggunaan AI [Lecture slides].
Universitas Muhammadiyah Purwokerto. - Reputable News & Technology Sources
BBC News. (2023). Articles on artificial intelligence, deepfake technology, and
digital ethics. https://www.bbc.com
Reuters. (2023). Reports on AI development and digital safety.
https://www.reuters.com
The Guardian. (2022–2024). AI regulation and digital ethics coverage.
https://www.theguardian.com
Kompas. (2023). Laporan perkembangan teknologi dan keamanan data di Indonesia.
https://www.kompas.com
Tempo. (2023). Artikel etika digital dan perlindungan data.
https://www.tempo.co
CNBC Indonesia. (2023–2024). Analisis perkembangan AI di Indonesia.
https://www.cnbcindonesia.com