Course Details

ARTIFICAL INTELLIGENCE AND R FOR COMPUTATIONAL APPLIED PSYCHOLOGY

PSYT335

Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
5PSYT335ARTIFICAL INTELLIGENCE AND R FOR COMPUTATIONAL APPLIED PSYCHOLOGY 3+0+035

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program PSYCHOLOGY
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The course aims to introduce students to the fundamental concepts and
practical applications of artificial intelligence (AI) and R programming in the
field of computational applied psychology. The course will cover various topics,
including the foundations of AI, supervised and unsupervised learning
techniques, natural language processing (NLP), and the use of R for data
analysis and visualization. Additionally, the course will explore case studies
and applications of AI in computational applied psychology, allowing students
to understand how these technologies are leveraged to address psychological
challenges and improve outcomes.
Course Content Understanding the principles and applications of AI in computational applied
psychology, including the terminology and concepts.
Gaining proficiency in applying supervised and unsupervised learning
techniques using R, preprocessing data, training machine learning models, and
interpreting the results.
Utilizing natural language processing (NLP) for psychological text analysis,
performing tasks such as sentiment analysis and text classification.
Developing skills in effectively using R for data analysis and visualization,
employing tools like ggplot2 to create visual representations of psychological
data and communicate findings.
Course Methods and Techniques Course Structure:
• Weekly topics and objectives
• Hands-on assignments and projects
• Case studies and real-world applications
• Ethical considerations in AI
Prerequisites and co-requisities ( PSYC101 ) and ( PSYC102 ) and ( PSYC103 ) and ( PSYC104 )
Course Coordinator None
Name of Lecturers None
Assistants Research Assist. Ömer Topuz https://avesis.agu.edu.tr/omer.topuz omer.topuz@agu.edu.tr
Research Assist. H. Öznur Erem https://avesis.agu.edu.tr/oznur.erem oznur.erem@agu.edu.tr
Work Placement(s) No

Recommended or Required Reading
Resources Each week class documentation and coding will be provided through CANVAS
Why AI in Psychology?
Artificial Intelligence (AI) is becoming increasingly significant in social psychology, transforming how we study group behavior, social interactions, and cultural dynamics. As AI continues to grow across various fields, its potential to reshape social psychology is particularly promising. AI allows researchers to analyze large datasets from social media, surveys, and experiments, uncovering patterns in human behavior that were previously difficult to detect. For instance, AI can be used to study group dynamics, predict trends in societal attitudes, and model the spread of misinformation or social influence. These real-world applications show AI’s potential to enhance our understanding of complex social behaviors, providing deeper insights into human interactions and contributing to more robust psychological theories.

Course Category
Mathematics and Basic Sciences %50
Engineering %30
Engineering Design %10
Social Sciences %10

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Quiz/Küçük Sınav 3 % 20
Ödev 1 % 20
Laboratuar 4 % 25
Final examination 1 % 20
Uygulama Çalışmaları (Laboratuar,Sanal Mahkeme,Stüdyo Çalışmaları vb.) 4 % 15
Total
13
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Yazılı Sınav 4 3 12
Sınıf İçi Aktivitesi 13 1 13
Final Teslimi ve Jüri 1 5 5
Ders Dışı Sınav 4 12 48
Rapor 4 10 40
Yazılım Deneyimi 4 8 32
Total Work Load   Number of ECTS Credits 5 150

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Interpret the principles and applications of AI in computational applied psychology.
2 Articulate supervised and unsupervised learning techniques using R.
3 Devise natural language processing (NLP) for psychological text analysis.
4 Programming R for data analysis and visualization.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Artificial Intelligence and Computational Applied Psychology NA
2 Foundations of Artificial Intelligence NA
3 R basics (Installations, basic operations, data structure and visualisation) To get started with R, you need to install it on your computer. You can download R from the official website (https://www.r-project.org/). Additionally, you may want to use an integrated development environment (IDE) for R, such as RStudio (https://www.rstudio.com/), which provides a more user-friendly interface.
4 Basic R Functions with Explanation and Examples Practicing and familiarity with R enviroment
5 Data Manipulation, Statistics and Distributions. Explanation: R provides functions for various probability distributions, such as the normal distribution (dnorm()), Poisson distribution (dpois()), and others. These functions allow you to calculate probabilities and densities for specific distributions. Re-generate lab practicing and coding exercise
6 Data Manipulation Functions and Practices NA
7 Getting to know secondary data Tasks: Load and Inspect Data: Load the “iris” dataset and inspect the first few rows to understand its structure. Data Summary: Calculate summary statistics for the numeric variables in the dataset, including mean, median, minimum, maximum, and quartiles. Species Distribution: Determine the distribution of iris species in the dataset. How many observations are there for each species? Data Visualization: Create a scatter plot to visualize the relationship between sepal length and sepal width. Use different colors or shapes to represent each iris species. Subset and Compare: Create two subsets of the data: one for versicolor species and one for virginica species. Compare the mean petal length between these two subsets. Conclusion: Based on the analysis, draw conclusions about the characteristics of the iris dataset and the relationships between different variables.
8 Practice Data Manipulation and Reporting Data NA
9 Fall Break NA
10 Between Subject ANOVA + Practice exam Code practicing
11 More about variance analyses NA
12 Natural Language Processing (NLP) Lab code practicing and reporting
13 Practical Exam Test Exam preparation
14 Recap AI & Psychology NA
15 Recap for R coding and Analysis Report NA


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
C1 5 3 1 3 1
C2 2 5 3 3 5 5 3 5 5 5
C3 3 5 2 5 4 5 4 5 2
C4 2 4 4 2 5 5 4 2 3

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant


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