The recent emergence of AI-driven code generation models can potentially transform programming education. To pinpoint the current state of research on using AI code generators to support learning and teaching programming, we conducted a systematic literature review with 21 papers published since 2018. The review presents the teaching and learning practices in programming education that utilize these models, the characteristics and performance indicators of the code generation models, and aspects to be considered when utilizing the models in programming education, including the risks and challenges of using code generation models for educational practices. AI code generators can be an assistive tool for both learners and instructors if the risks are mitigated.

In recent years, significant progress has been made in the field of natural language processing (NLP) through the development of large language models (LLMs) like BERT and ChatGPT. These models have showcased remarkable abilities across a range of NLP tasks. However, effectively harnessing their potential requires meticulous prompt engineering and a comprehensive understanding of their limitations.

Additionally, LLMs have attracted attention in the educational domain for their potential to enhance learning and teaching experiences, particularly in fostering the development of computational thinking skills.
This paper aims to explore the potential of leveraging NLP and prompt engineering techniques to generate successful solutions to coding problems following initial failures. Furthermore, the research explores the potential applications of NLP techniques in teaching and learning practices involving LLMs and their potential drawbacks in this context.

Using AI to support programming has recently gained a lot of popularity. Researchers have been developing tools to support programming activities using GPT models such as ChatGPT and Codex In this paper, we present the most common pro-gramming activities that these models can support. We show that they have a varying range of success
across solving Code Generation, Code Explana-tion,and Data Visualisation problems, but are often able to solve around 50% of problems on the first try. Multiple tries can raise these averages to 75%. Additionally, specialized tools using GPT models have seen promising results regarding Data Visuali-sation, Software Vulnerability Detection, and Gen-
eral Programming Support. This shows a promis-ing trend, and can mean we will all be pair pro-gramming with AI in the near-future.

Code generation is becoming one of the most important tools in an Integrated Development Environment (IDE) for programmers, be they novices or experts. It allows them to produce code faster, avoid typos and other mistakes, be more efficient and easily turn ideas into code. As such, there are many in-IDE code generation models to choose from, each with their own promises, functionalities and implementation techniques. This report presents the results of a literature survey into the code generation models that have been integrated into programming environments and serves as an overview and analysis of them. The results of this paper provide a set of guidelines for code generation that have been summarized from the selected literature.

Due to the increase in student numbers, the amount of time teachers have for each individual decreases. To keep the quality of the program the same, learning analytics can be utilized to assist teachers to give a better overview of problems which the students are struggling the most with. This work makes use of WebLab data of the Introduction to Python course. To provide more useful information for teachers in introductory programming assignments, this work applies learning analytics to revision history of the student submissions of this course. The revision history provides more insight into the progression of the students throughout the assignment compared to only the final solutions. With these data, this work clusters and analyzes student submissions to provide instructors with information about the types of submitted solutions, students who struggled with the assignment, and the biggest hurdles for the students. Results show that the clustering of student submissions is 90\% accurate in comparison to manually clustered solutions, however the identification of students who struggled with the assignment should take student behaviour more into account. Because, student behaviour has been discussed in the focus group as being a major factor in whether data can indicate whether a student is struggling or not. Further research should be directed into expanding the proposed algorithms to more complex programming assignments and improving the way the data is visualized to the teachers.