WGU D842 CON1 : ETHICAL DECISIONS ON
STUDENT PRIVACY AND AI IN EDUCATION
COMPREHENSIVE STUDY GUIDE 2026 FULL
QUESTIONS AND SOLUTIONS GRADED A+
◍ Data centers + ethical issues.
Answer: Huge facilities that store/process data on servers.
◍ What was the outcome of the data analysis conducted by Hephaestats?.
Answer: Hephaestats produced synthetic data and identified key indicators
predicting dropout.
◍ What was the response from Minerva High School representatives to the
concerns raised?.
Answer: They met with concerned students, parents, and teachers
◍ Key point about representation.
Answer: Representations are always selective (show some stuff, hide other
stuff).
◍ What was the approach taken by Hephaestats to analyze student data?.
Answer: Hephaestats analyzed a wide range of predictors, including
demographics, academic factors, and teacher statistics.
◍ What ethical considerations arise from treating minors as less autonomous
than adults?.
Answer: It raises questions about paternalism and the balance between
protection and respect for individual agency.
◍ Ethical issues with data centers.
Answer: Energy use, water for cooling, pollution, local community impacts,
who benefits vs who pays costs.
, ◍ GOFAI (old-school symbolic AI).
Answer: Humans write explicit rules (if/then logic).
◍ Example of technology changing behavior.
Answer: Smartphones make constant contact easy → people check phones
constantly → attention changes, social norms change.
◍ Main takeaway from 'secret life of data'.
Answer: Data about 'you' often becomes data about others, and it can be
used later.
◍ What was one of the criticisms regarding the enthusiasm for AI in
education?.
Answer: It was seen as over-enthusiastic and redundant to teachers' insights
◍ What is the relationship between teacher statistics and student dropout
rates?.
Answer: Teacher certifications, degrees, and experience can influence
student success and dropout rates.
◍ Surveillance capitalism.
Answer: Companies profit by collecting behavior data to predict and
influence what you do.
◍ Epistemological vs normative concerns.
Answer: Epistemological = 'Is it true / reliable?' Normative = 'Is it right /
fair?'
◍ Data vs information (critical view).
Answer: Data is never purely raw because humans choose what to collect +
how to categorize.
◍ What types of factors did Hephaestats identify as predictors of dropout?.
Answer: Factors included administrative issues, external responsibilities,
and previously unconsidered factors like nutrition.
◍ What is the relationship between AI ethics and the design of AI systems?.
Answer: The design reflects the values and priorities of its creators,
STUDENT PRIVACY AND AI IN EDUCATION
COMPREHENSIVE STUDY GUIDE 2026 FULL
QUESTIONS AND SOLUTIONS GRADED A+
◍ Data centers + ethical issues.
Answer: Huge facilities that store/process data on servers.
◍ What was the outcome of the data analysis conducted by Hephaestats?.
Answer: Hephaestats produced synthetic data and identified key indicators
predicting dropout.
◍ What was the response from Minerva High School representatives to the
concerns raised?.
Answer: They met with concerned students, parents, and teachers
◍ Key point about representation.
Answer: Representations are always selective (show some stuff, hide other
stuff).
◍ What was the approach taken by Hephaestats to analyze student data?.
Answer: Hephaestats analyzed a wide range of predictors, including
demographics, academic factors, and teacher statistics.
◍ What ethical considerations arise from treating minors as less autonomous
than adults?.
Answer: It raises questions about paternalism and the balance between
protection and respect for individual agency.
◍ Ethical issues with data centers.
Answer: Energy use, water for cooling, pollution, local community impacts,
who benefits vs who pays costs.
, ◍ GOFAI (old-school symbolic AI).
Answer: Humans write explicit rules (if/then logic).
◍ Example of technology changing behavior.
Answer: Smartphones make constant contact easy → people check phones
constantly → attention changes, social norms change.
◍ Main takeaway from 'secret life of data'.
Answer: Data about 'you' often becomes data about others, and it can be
used later.
◍ What was one of the criticisms regarding the enthusiasm for AI in
education?.
Answer: It was seen as over-enthusiastic and redundant to teachers' insights
◍ What is the relationship between teacher statistics and student dropout
rates?.
Answer: Teacher certifications, degrees, and experience can influence
student success and dropout rates.
◍ Surveillance capitalism.
Answer: Companies profit by collecting behavior data to predict and
influence what you do.
◍ Epistemological vs normative concerns.
Answer: Epistemological = 'Is it true / reliable?' Normative = 'Is it right /
fair?'
◍ Data vs information (critical view).
Answer: Data is never purely raw because humans choose what to collect +
how to categorize.
◍ What types of factors did Hephaestats identify as predictors of dropout?.
Answer: Factors included administrative issues, external responsibilities,
and previously unconsidered factors like nutrition.
◍ What is the relationship between AI ethics and the design of AI systems?.
Answer: The design reflects the values and priorities of its creators,
