WGU D842 CON1 : ETHICAL DECISIONS ON
STUDENT PRIVACY AND AI IN EDUCATION
FINAL TEST 2026 QUESTIONS WITH
CORRECT ANSWERS GRADED A+
◍ Ethical questions of big data and algorithms.
Answer: The use of algorithms with big data raises concerns about privacy,
bias, and accountability, differing from traditional data uses.
◍ The five Vs of data.
Answer: Volume, Variety, Velocity, Veracity, and Value; these describe the
characteristics and challenges of managing big data.
◍ Epistemological vs. normative concerns.
Answer: Epistemological concerns relate to knowledge and understanding,
while normative concerns relate to values and ethics in the use of
algorithms.
◍ Generative AI.
Answer: A type of AI that can generate new content based on learned
patterns from existing data.
◍ Value-neutrality thesis.
Answer: The idea that technology itself is neutral and does not inherently
carry moral values.
◍ GOFAI vs. PDP/connectionist models.
Answer: GOFAI (Good Old-Fashioned Artificial Intelligence) uses
rule-based systems, while PDP (Parallel Distributed Processing) and
connectionist models use neural networks.
◍ If training data contains biases or stereotypes, AI systems may perpetuate
, and amplify these biases..
Answer: How can biased training data impact AI outputs and
decision-making?
◍ Technology's impact on behavior.
Answer: According to Morrow, technology alters human behavior and can
lead to various consequences, both positive and negative.
◍ Secret life of data.
Answer: The hidden implications and uses of data, illustrated by case studies
that reveal how data is collected and utilized.
◍ precision.
Answer: the quality of AI responses that accurately address the specific
requirements or queries outlined in the prompt, minimizing errors or
irrelevant information
◍ various domains.
Answer: different fields or industries where AI systems are applied, such as
healthcare, customer service, education, and finance, each with unique
requirements and challenges in AI interaction
◍ The primary function of generative AI prompts in natural language
processing (NLP) is generating text-based responses that enable
understanding and analysis of unstructured data..
Answer: What is the primary function of generative AI prompts in natural
language processing (NLP)?
◍ Value-laden technology.
Answer: The concept that technology is influenced by the values and biases
of its creators and users.
◍ Data as representations of interests and values.
Answer: Data reflects the priorities and biases of those who collect and
interpret it.
◍ This is an example of Tree-of-Thought Prompting.
, Answer: Match the example prompts with the technique:Write a short story
about a detective solving a mysterious mistaken identity case."
◍ Authenticity.
Answer: The quality of being genuine or true, with distinctions made
between deep and shallow inauthenticity.
◍ unsupervised learning.
Answer: a type of machine learning where the model is trained on unlabeled
data without explicit guidance or supervision
◍ Suitable for educational settings, question-answering systems, and
conversational AI applications where users seek information on common
topics..
Answer: Generated Knowledge Prompting
◍ output format.
Answer: the structure, layout, and presentation style of the results or outputs
generated by a system, model, or process
◍ Clickwrap.
Answer: A type of online agreement where users must click to accept terms
before accessing a service.
◍ Data for generative AI training.
Answer: The sources of data used to train generative AI models often raise
ethical questions regarding consent and ownership.
◍ Data.
Answer: Raw facts and figures that can be processed to produce
information.
◍ Data center.
Answer: A facility used to house computer systems and associated
components, which raises ethical issues such as data privacy and
environmental impact.
◍ Relationship between data and information.
, Answer: Data can be transformed into information through processing, and
information can provide context to data.
◍ Informational privacy.
Answer: The right of individuals to control their personal information and
how it is used.
◍ Multi-stakeholder collaboration fosters responsible AI by bringing diverse
perspectives together to address ethical challenges..
Answer: How does collaboration among various stakeholders support ethical
AI development?
◍ ambiguity.
Answer: lack of clarity or specificity in prompts or queries, leading to
potential confusion or multiple interpretations by AI systems.
◍ Metaphors in understanding data.
Answer: Metaphors, like 'Data is the new oil,' help conceptualize complex
ideas by relating them to familiar concepts.
◍ Blurring fact and fiction.
Answer: The phenomenon where the distinction between reality and
falsehood becomes unclear, impacting the value of truth.
◍ Algorithms.
Answer: Step-by-step procedures or formulas for solving problems, often
used in computing and data processing.
◍ Explainable AI (XAI) provides understandable justifications for AI
decisions and supports accountability..
Answer: Why is transparency (or explainability) important in AI systems?
◍ Representation.
Answer: A way to depict or symbolize data, which can have characteristics
such as accuracy, relevance, and bias.
◍ Surveillance capitalism.
Answer: An economic system centered around the commodification of
STUDENT PRIVACY AND AI IN EDUCATION
FINAL TEST 2026 QUESTIONS WITH
CORRECT ANSWERS GRADED A+
◍ Ethical questions of big data and algorithms.
Answer: The use of algorithms with big data raises concerns about privacy,
bias, and accountability, differing from traditional data uses.
◍ The five Vs of data.
Answer: Volume, Variety, Velocity, Veracity, and Value; these describe the
characteristics and challenges of managing big data.
◍ Epistemological vs. normative concerns.
Answer: Epistemological concerns relate to knowledge and understanding,
while normative concerns relate to values and ethics in the use of
algorithms.
◍ Generative AI.
Answer: A type of AI that can generate new content based on learned
patterns from existing data.
◍ Value-neutrality thesis.
Answer: The idea that technology itself is neutral and does not inherently
carry moral values.
◍ GOFAI vs. PDP/connectionist models.
Answer: GOFAI (Good Old-Fashioned Artificial Intelligence) uses
rule-based systems, while PDP (Parallel Distributed Processing) and
connectionist models use neural networks.
◍ If training data contains biases or stereotypes, AI systems may perpetuate
, and amplify these biases..
Answer: How can biased training data impact AI outputs and
decision-making?
◍ Technology's impact on behavior.
Answer: According to Morrow, technology alters human behavior and can
lead to various consequences, both positive and negative.
◍ Secret life of data.
Answer: The hidden implications and uses of data, illustrated by case studies
that reveal how data is collected and utilized.
◍ precision.
Answer: the quality of AI responses that accurately address the specific
requirements or queries outlined in the prompt, minimizing errors or
irrelevant information
◍ various domains.
Answer: different fields or industries where AI systems are applied, such as
healthcare, customer service, education, and finance, each with unique
requirements and challenges in AI interaction
◍ The primary function of generative AI prompts in natural language
processing (NLP) is generating text-based responses that enable
understanding and analysis of unstructured data..
Answer: What is the primary function of generative AI prompts in natural
language processing (NLP)?
◍ Value-laden technology.
Answer: The concept that technology is influenced by the values and biases
of its creators and users.
◍ Data as representations of interests and values.
Answer: Data reflects the priorities and biases of those who collect and
interpret it.
◍ This is an example of Tree-of-Thought Prompting.
, Answer: Match the example prompts with the technique:Write a short story
about a detective solving a mysterious mistaken identity case."
◍ Authenticity.
Answer: The quality of being genuine or true, with distinctions made
between deep and shallow inauthenticity.
◍ unsupervised learning.
Answer: a type of machine learning where the model is trained on unlabeled
data without explicit guidance or supervision
◍ Suitable for educational settings, question-answering systems, and
conversational AI applications where users seek information on common
topics..
Answer: Generated Knowledge Prompting
◍ output format.
Answer: the structure, layout, and presentation style of the results or outputs
generated by a system, model, or process
◍ Clickwrap.
Answer: A type of online agreement where users must click to accept terms
before accessing a service.
◍ Data for generative AI training.
Answer: The sources of data used to train generative AI models often raise
ethical questions regarding consent and ownership.
◍ Data.
Answer: Raw facts and figures that can be processed to produce
information.
◍ Data center.
Answer: A facility used to house computer systems and associated
components, which raises ethical issues such as data privacy and
environmental impact.
◍ Relationship between data and information.
, Answer: Data can be transformed into information through processing, and
information can provide context to data.
◍ Informational privacy.
Answer: The right of individuals to control their personal information and
how it is used.
◍ Multi-stakeholder collaboration fosters responsible AI by bringing diverse
perspectives together to address ethical challenges..
Answer: How does collaboration among various stakeholders support ethical
AI development?
◍ ambiguity.
Answer: lack of clarity or specificity in prompts or queries, leading to
potential confusion or multiple interpretations by AI systems.
◍ Metaphors in understanding data.
Answer: Metaphors, like 'Data is the new oil,' help conceptualize complex
ideas by relating them to familiar concepts.
◍ Blurring fact and fiction.
Answer: The phenomenon where the distinction between reality and
falsehood becomes unclear, impacting the value of truth.
◍ Algorithms.
Answer: Step-by-step procedures or formulas for solving problems, often
used in computing and data processing.
◍ Explainable AI (XAI) provides understandable justifications for AI
decisions and supports accountability..
Answer: Why is transparency (or explainability) important in AI systems?
◍ Representation.
Answer: A way to depict or symbolize data, which can have characteristics
such as accuracy, relevance, and bias.
◍ Surveillance capitalism.
Answer: An economic system centered around the commodification of
