Technology Assignment
theedukator2020
EXAMPLE.pdf
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ARTIFICIAL INTELLIGENCE: TRENDS, RISKS, AND POTENTIAL BENEFITS
What option are you completing for this assignment? Option 1
Step 1: KWL Chart
Know What do you already know?
Want to Know What do you want to know more
about?
Learned As a result of completing this assignment, what have
you learned?
Evidence Provide citations for the facts you listed
in the Learned column
AI is the abbreviation for artificial intelligence.
AI is a very broad term. What does it include?
AI uses computers to generate information that would normally require human interaction (IBM, n.d.). AI uses data to determine the information that is requested, and the data usually exceeds human capability (IBM, n.d.). It can span disciplines, uses algorithms, and has far more components than just asking questions (IBM, n.d.)
IBM (n.d.). What is strong AI? Https://ibm.com. Retrieved August 31, 2024, from https://www.ibm.com/topics/stron g-ai
AI is trending in education. How can AI improve learning without comprising traditional pedagogies?
AI can assist teachers in daily tasks, such as grading, allowing teachers time to focus on instructional strategies (Ghamwari et al., 2024). AI can improve student engagement and parental communication. (Ghamwari et al., 2024).
Ghamwari, N., Shal, T., & Ghamwari, N. A. (2024). Exploring the impact of AI on teacher leadership: Regressing or expanding? Education and Information Technologies, 29, 8415-8433. https://doi.org/10.1007/s10639- 023-12174-w
There is concern about privacy.
Does using AI compromise a person’s private information?
AI does make connections with a person’s identity but generally does not significantly impact one’s identity (Elliott & Soiffer, 2022).
Elliott, D., & Soiffer, E. (2022). AI technologies, privacy, and security. Hypothesis and Theory, 5, 1- 8. https://doi.org/10.3389/frai.2022 .826737
AI uses algorithms to Is the information generated by Studies show that AI generated information Khalifa, M., & Albadawy,
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generate information. AI accurate? has varying degrees of accuracy, and some information may be incorrect or incomplete (Khalifa & Albadawy, 2024).
M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 5, 1- 11. https://doi.org/10.1016/j.cmpb up.2024.100145
AI is used to quickly obtain information and is supposed to work like the human mind.
What are the limitations of AI? Accuracy is still a small limitation of AI so it is necessary to check the content quality (Klaif et al., 2023). In terms of using AI for research and writing, AI, specifically ChatGPT, many inaccuracies have been noted (Klaif et al., 2023).
Khlaif, Z. N., Mousa, A., Hattab, M. K., Itmazi, J., Hassan, A. A., Sanmugam, M., & Ayyoub, A. (2023). The Potential and concerns of using AI in scientific research: ChatGPT performance evaluation. JMIR Medical Education, 9, e47049. https://doi.org/10.2196/47049
AI uses computers and applications to perform tasks to assist learning or to obtain information.
Does AI impede traditional learning?
The use of AI does have positive and negative impacts on learning (Karan & Angadi, 2023). It is suggested that overutilization of AI can decrease critical thinking skills but increase technology skills (Karan & Angadi, 2023).
Karan, B., & Angadi, G. R. (2023). Potential risks of artificial intelligence integration into School education: A systematic review. Bulletin of Science, Technology & Society, 43(3-4), 67-85. https://doi.org/10.1177/027046762 31224705
AI assistants are becoming increasingly popular.
Does using an AI assistant impact social-emotional relationships?
AI integration can reduce the personalized teaching approach and affect teacher-student relationships, thus negatively impacting learning (Karan & Angadi, 2023).
Karan, B., & Angadi, G. R. (2023). Potential risks of artificial intelligence integration into School education: A systematic review. Bulletin of Science, Technology & Society, 43(3-4), 67-85. https://doi.org/10.1177/027046762 31224705
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Step 2.
Term Definition Evidence Provide citations for the definitions you listed
Machine Learning
Machine learning refers to a field of computer science in which people teach a computer system to identify patterns and make predictions (Microsoft Source: 10 AI Terms Everyone Should Know - 10 AI Terms, n.d.). Machine learning combines techniques to create an application that can learn from a data set or the user; a broad set of processing techniques (Jones, 2020).
Microsoft Source: 10 AI terms Everyone should know - 10 AI terms. (n.d.). 10 AI Terms. Retrieved August 31, 2024, from https://news.microsoft.com/10-ai-terms/
Jones, A. (2020, March 5). 12 artificial intelligence terms that every marketer should know. https://catalyst.iabc.com. https://catalyst.iabc.com/Articles/12-artificial- intelligence-terms-that-every-marketer-should- know
Multimodal model
Multimodal models use different types of data at the same time and combine the information to answer questions (Microsoft Source: 10 AI Terms Everyone Should Know - 10 AI Terms, n.d.). The model uses images, text, and video to process information (Multimodal AI, n.d.)
Microsoft Source: 10 AI terms Everyone should know - 10 AI terms. (n.d.). 10 AI Terms. Retrieved August 31, 2024, from https://news.microsoft.com/10-ai-terms/
Multimodal AI. (n.d.). Google Cloud. https://cloud.google.com/use-cases/multimodal- ai
Backward chaining
In backward chaining, the model starts with the result and uses it to find supportive data (50 AI terms every beginner should know, 2021). The system uses the final goal to determine what must be identified to achieve the goal (Difference between backward and forward chaining, 2020).
50 AI terms every beginner should know. (2021, March 1). telusinternational.com. https://www.telusinternational.com/insights/ai- data/article/50-beginner-ai-terms-you-should- know
Difference between backward and forward chaining. (2020, July 16). geeksforgeeks.org.
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https://www.geeksforgeeks.org/difference- between-backward-and-forward-chaining/
Bounding box A bounding box is an imaginary box found on images that are labeled to help a model recognize it (50 AI terms every beginner should know, 2021). A bounding box is used in image or video tagging (50 AI terms every beginner should know, 2021). A bounding box is used to annotate images and is drawn around an object or image (People for AI, 2024).
50 AI terms every beginner should know. (2021, March 1). telusinternational.com. https://www.telusinternational.com/insights/ai- data/article/50-beginner-ai-terms-you-should- know
People for AI. (2024, March 5). Bounding Box definition - People for AI. https://www.peopleforai.com/glossary/bounding- box/
Deep learning Deep learning simulates the human brain and learns from how data is structured instead of an algorithm (50 AI terms every beginner should know, 2021). It is part of the neural network, like scaffolded learning and uses the examples it obtains to learn about a topic (Jones, 2020).
50 AI terms every beginner should know. (2021, March 1). telusinternational.com. https://www.telusinternational.com/insights/ai- data/article/50-beginner-ai-terms-you-should- know
Jones, A. (2020, March 5). 12 artificial intelligence terms that every marketer should know. https://catalyst.iabc.com. https://catalyst.iabc.com/Articles/12-artificial- intelligence-terms-that-every-marketer-should- know
Data mining Data mining uses sets of data to find new patterns to improve the model (50 AI terms every beginner should know, 2021) by filtering large amounts of information, using raw information and modifying it for use (Jones, 2020).
50 AI terms every beginner should know. (2021, March 1). telusinternational.com. https://www.telusinternational.com/insights/ai- data/article/50-beginner-ai-terms-you-should- know
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Jones, A. (2020, March 5). 12 artificial intelligence terms that every marketer should know. https://catalyst.iabc.com. https://catalyst.iabc.com/Articles/12-artificial- intelligence-terms-that-every-marketer-should- know
Machine intelligence
Machine intelligence is a broad term for learning algorithms (50 AI terms every beginner should know, 2021). It is a part of AI and uses mathematical models (Maven, n.d.). Machine intelligence supports continuous learning and improvements (Maven, n.d.).
50 AI terms every beginner should know. (2021, March 1). telusinternational.com. https://www.telusinternational.com/insights/ai- data/article/50-beginner-ai-terms-you-should- know
Maven: The main differences between machine intelligence and artificial intelligence. (n.d.). https://maven.com/articles/machine-intelligence- vs-artificial-intelligence
Turing test The Turing test assesses the similarity of a machine and human participant (50 AI terms every beginner should know, 2021). The machine passes the test if what is produced is like that of a human (50 AI terms every beginner should know, 2021). The Turing test evaluates a machine’s intellectual ability to determine if it is similar to a human (Jones, 2020).
50 AI terms every beginner should know. (2021, March 1). telusinternational.com. https://www.telusinternational.com/insights/ai- data/article/50-beginner-ai-terms-you-should- know
Jones, A. (2020, March 5). 12 artificial intelligence terms that every marketer should know. https://catalyst.iabc.com. https://catalyst.iabc.com/Articles/12-artificial- intelligence-terms-that-every-marketer-should- know
Weak AI Weak AI, which is the type of AI that is most used, is a specialized skill 50 AI terms every beginner should know. (2021,
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set that focuses on particular task (50 AI terms every beginner should know, 2021). It focuses on specific, singular tasks like answering questions (IBM, n.d.).
March 1). telusinternational.com. https://www.telusinternational.com/insights/ai- data/article/50-beginner-ai-terms-you-should- know
IBM (n.d.). What is strong AI? Https://ibm.com. Retrieved August 31, 2024, from https://www.ibm.com/topics/strong-ai
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Step 3
After using Google Gemini to search the prompt “Give a brief history of the explosion of Artificial Intelligence, the response was copied and pasted below and in a separate word document. After clicking review and track changes, there were no changes needed to the information as indicated by the track changes feature.
The explosion of artificial intelligence (AI) can be traced back to the mid-20th century, when researchers began exploring the possibility of creating machines that could think and learn like humans. Early AI systems were limited by computational power and data availability, but significant breakthroughs in the late 20th and early 21st centuries propelled the field forward.
Key milestones in the history of AI include:
· 1950s: Alan Turing proposes the Turing Test, a measure of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.
· 1956: The Dartmouth Summer Research Project on Artificial Intelligence marks the birth of AI as a field of study.
· 1960s and 1970s: Early AI systems are developed, such as expert systems and natural language processing tools, but limitations in hardware and algorithms lead to a period of disillusionment known as the "AI winter."
· 1980s: AI research experiences a resurgence with the development of neural networks and machine learning techniques.
· 1990s and 2000s: Advances in computing power and data availability enable the development of more sophisticated AI systems, such as IBM's Deep Blue, which defeated world chess champion Garry Kasparov in 1997.
· 2010s: Deep learning, a type of machine learning that uses neural networks with multiple layers, becomes a dominant force in AI. Breakthroughs in deep learning lead to significant advancements in areas such as image recognition, natural language processing, and self-driving cars.
The explosion of AI in recent years has been driven by a combination of factors, including:
· Increased computing power: The availability of powerful computers has made it possible to train large and complex AI models.
· Vast amounts of data: The growth of the internet and digital technologies has generated massive amounts of data that can be used to train AI systems.
· Advances in algorithms: Researchers have developed new algorithms that enable AI systems to learn and improve on their own.
The explosion of AI has led to the development of a wide range of applications, from virtual assistants and recommendation systems to medical diagnosis and autonomous vehicles. As AI continues to advance, it is likely to have a profound impact on society and the economy in the years to come.
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Step 4.
There are advantages and disadvantages to the implementation of AI use in the
classroom. AI can assist teachers in daily tasks, such as grading, allowing teachers time to focus
on instructional strategies (Ghamwari et al., 2024). AI can improve student engagement and
parental communication. (Ghamwari et al., 2024). By using AI for such tasks, teachers can
spend more time developing interpersonal relationships with students and focusing on quality
instruction. Generative AI can be useful for creating lesson plans and assessments (Powell &
Courchesne, 2024). ChatGPT can create differentiated lesson plans and resources and use them
as a starting point for lessons (Powell & Courchesne, 2024).
AI has advantages and disadvantages in society as well. There is concern that AI will
replace the need for interpersonal interactions, as face to face communication will no longer be
needed (Tai, 2020). There are also concerns that AI will reduce employment as AI will be able to
do certain jobs, thus reducing the need for traditional workers (Tai, 2020). There are also ethical
concerns with overreliance on AI, for example using AI for racial bias (Tai, 2020). AI can be a
powerful tool but should be balanced with interpersonal relationships (Ghamrawi et al., 2023).
As useful as AI can be in technology, education, and society, studies show that AI
generated information has varying degrees of accuracy, and some information may be incorrect
or incomplete (Khalifa & Albadawy, 2024). It is suggested that overutilization of AI can
decrease critical thinking skills but increase technology skills (Karan & Angadi, 2023). Studies
show that AI may reflect human preconceived notions, such as racism (Tai, 2020). There is also
a concern that AI could create a security risk to individuals but while AI does make connections
with a person’s identity but generally does not significantly impact one’s identity (Elliott &
Soiffer, 2022).
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Some concerns about AI integration include economic impact, bias, public health,
privacy, and inequality (Khogali & Mekid, 2023). AI developments in automation may displace
workers without appropriate skills (Khogali & Mekid, 2023). By providing training to
individuals, the concern can be offset but equity needs to be examined. Ensuring individual
privacy and data is paramount when AI systems collect, analyze, and utilize personal data. There
are programs that can address concerns about user data being collected, such as Apple’s App
Tracking Transparency and Global Privacy Control (Miller, 2024). Regulating AI could help
protect user privacy and prevent personal data from being exposed but also could reduce or
prevent any AI bias (Miller, 2024). Shifting from opt-out data collection to opt-in data collection
could help facilitate privacy by defaulting to private settings (King & Meinhardt, 2024).
School policies should adjust to the changes in the AI landscape as more AI infusion
occurs within the classroom. Using AI to assess students is convenient but unless there is
certainty that the user’s bias is not intertwined, it may not be as effective and timely as hoped.
Because of privacy and ethical concerns, divisions should develop policies that evolve with the
advancements of AI in the classrooms. Those policies would need to continually adapt to
changes, requiring constant revisions to meet the needs of students. The lack of interpersonal
relationships impacts learning and AI does not replace interpersonal communication (Ghamwari
et al., 2024).
AI systems are not inherently ethical or moral (DeCremer & Narayanan, 2023). AI
cannot currently replace human judgement in moral or ethical situations (DeCremer &
Narayanan, 2023). Ethics and morality are a limitation with AI. Being able to adapt and make
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ethical decisions are something that AI is not capable of doing (DeCremer & Narayanan, 2023).
Furthermore, AI does not have the capability to determine what is morally appropriate or socially
acceptable (DeCremer & Narayanan, 2023). Even without being able to determine morality or
ethics, AI can provide support based on emotion. The recognition of facial cues by AI can alert
teachers to provide empathy and support (Vistorte et al., 2024). The emotional recognition
provided by AI are related to facial expressions and eye movement so monitoring systems can
identify real-time emotions (Vistorte et al., 2024). This can provide a teacher with pertinent
information about engagement, peer interactions and academic achievement (Vistorte et al.,
2024).
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References
50 AI terms every beginner should know. (2021, March 1). telusinternational.com.
https://www.telusinternational.com/insights/ai-data/article/50-beginner-ai-terms-you-
should-know
De Cremer, D., & Narayanan, D. (2023). How AI tools can-and cannot-help organizations
become more ethical. Frontiers in artificial intelligence, 6, 1093712.
https://doi.org/10.3389/frai.2023.1093712
Elliott, D., & Soiffer, E. (2022). AI technologies, privacy, and security. Hypothesis and Theory,
5, 1-8. https://doi.org/10.3389/frai.2022.826737
Ghamwari, N., Shal, T., & Ghamwari, N. A. (2024). Exploring the impact of AI on teacher
leadership: Regressing or expanding? Education and Information Technologies, 29,
8415-8433. https://doi.org/10.1007/s10639-023-12174-w
IBM (n.d.). What is strong AI? Https://ibm.com. Retrieved August 31, 2024, from
https://www.ibm.com/topics/strong-ai
Jones, A. (2020, March 5). 12 artificial intelligence terms that every marketer should know.
https://catalyst.iabc.com. https://catalyst.iabc.com/Articles/12-artificial-intelligence-
terms-that-every-marketer-should-know
Karan, B., & Angadi, G. R. (2023). Potential risks of artificial intelligence integration into
School education: A systematic review. Bulletin of Science, Technology & Society, 43(3-
4), 67-85. https://doi.org/10.1177/02704676231224705
King, J., & Meinhardt, C. (2024, February 22). Rethinking privacy in the AI era: Policy
provocations for a data-centric world. Human Centered Artificial Intelligence Stanford
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University. https://hai.stanford.edu/white-paper-rethinking-privacy-ai-era-policy-
provocations-data-centric-world
Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and
research: An essential productivity tool. Computer Methods and Programs in
Biomedicine Update, 5, 1-11. https://doi.org/10.1016/j.cmpbup.2024.100145
Khlaif, Z. N., Mousa, A., Hattab, M. K., Itmazi, J., Hassan, A. A., Sanmugam, M., & Ayyoub, A.
(2023). The Potential and concerns of using AI in scientific research: ChatGPT
performance evaluation. JMIR Medical Education, 9, e47049.
https://doi.org/10.2196/47049
Khogali, H., & Mekid, S. (2023). The blended future of automation and AI: Examining some
long-term societal and ethical impact features. Technology and Society, 73, 1-
12. https://doi.org/10.1016/j.techsoc.2023.102232
Maven: The main differences between machine intelligence and artificial intelligence. (n.d.).
https://maven.com/articles/machine-intelligence-vs-artificial-intelligence
Microsoft Source: 10 AI terms Everyone should know - 10 AI terms. (n.d.). 10 AI Terms.
Retrieved August 31, 2024, from https://news.microsoft.com/10-ai-terms/
Miller, K. (2024, March 18). Privacy in an AI era: How do we protect our personal
information? Human Centered Artificial Intelligence Stanford
University. https://hai.stanford.edu/news/privacy-ai-era-how-do-we-protect-our-personal-
information
Multimodal AI. (n.d.). Google Cloud. https://cloud.google.com/use-cases/multimodal-ai
People for AI. (2024, March 5). Bounding Box definition - People for AI.
https://www.peopleforai.com/glossary/bounding-box/
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Powell, W., & Courchesne, S. (2024). Opportunities and risks involved in using ChatGPT to
create first grade science lesson plans. PloS one, 19(6), e0305337.
https://doi.org/10.1371/journal.pone.0305337
Tai M. C. (2020). The impact of artificial intelligence on human society and bioethics. Tzu chi
medical journal, 32(4), 339–343. https://doi.org/10.4103/tcmj.tcmj_71_20
Tornero-Costa, R., Martinez-Millana, A., Azzopardi-Muscat, N., Lazeri, L., Traver, V., &
Novillo-Ortiz, D. (2023). Methodological and Quality Flaws in the Use of Artificial
Intelligence in Mental Health Research: Systematic Review. JMIR mental health, 10,
e42045. https://doi.org/10.2196/42045
Vistorte, A. O. R., Deroncele-Acosta, A., Ayala, J. L. M., Barrasa, A., López-Granero, C., &
Martí-González, M. (2024). Integrating artificial intelligence to assess emotions in
learning environments: a systematic literature review. Frontiers in psychology, 15,
1387089. https://doi.org/10.3389/fpsyg.2024.1387089
