This is a study of the impacts of the incorporation, into certain points of the Computer Science degree program at the University of New Orleans, of Course Management software with an Autograding component. The software in question, developed at Carnegie Mellon University, is called “Autolab.” We begin by dissecting Autolab in order to gain an understanding of its inner workings. We can then take out understanding of its functionality and apply that to an examination of fundamental changes to courses in the time since they incorporated the software. With that, we then compare Drop, Failure, Withdrawal rate data from before and after the introduction of Autolab. With this collection of data, we can conclude, to a certain extent, that Autolab has had a negligible impact on course outcomes, but a measurable impact on course structure and pedagogy as well as improved quality of life for students and professors, alike.

Denna strukturerade litteraturstudie undersöker vilka verktyg och tekniker relaterade tillartificiell intelligens (AI) som kan understödja lärare genom att överta repetitiva ochtidskrävande administrativa uppgifter som ligger utanför den direkta interaktionen mellanlärare och student. Genom att granska tillgänglig forskning inom detta område kartläggervi dessa. Denna kartläggning följs sedan av en diskussion om resultatet och potentialen iden senaste tekniken samt en fingervisning om var ytterligare forskning kan ta vid. För att kartlägga befintlig forskning i ämnet har denna litteraturstudie genomförts medutgångspunkt i en generell kurs uppdelad i tre faser: före, under och efter. På grund avden potentiella omfattningen av detta område undersökte vi ett fall per fas, respektivekursdesign, rättning och kursutvärdering. Litteraturstudien är begränsad till högre utbild-ningsinstitutioner såsom universitet och högskolor. Våra resultat visar att majoriteten av de AI-verktyg och -tekniker som används inomdessa tre områden är baserade på naturlig språkbearbetning (NLP) och neurala nätverk.Vi fann att det finns en större mängd forskning inom rättning jämfört med kursdesign ochkursutvärdering, och att forskningen inom alla dessa områden ökar över tiden. Vi noterardock en brist på forskning som undersöker den praktiska användningen av dessa verktygoch tekniker av lärare. / This structured literature study examines which tools and technologies related to artificialintelligence (AI) that can support teachers by replacing repetitive and time-consumingadministrative tasks that are outside of the direct interaction between teacher and student. By reviewing available research in this field, we map these findings. This is then followedby a discussion of the results and potential of the latest technology, as well as an indicationof where further research can take place.To map existing research on the subject, this literature study has been conducted basedon a general course divided into three phases: before, during, and after. Due to the po-tential size of this area, we investigated only one case per each phase, respektively coursedesign, grading and course evaluation. The literature study is limited to higher educationinstitutions such as universities and colleges. Our results shows that the majority of AI techniques and tools used in these three areasare based on natural language processing (NLP) and neural networks. We found that thereis a greater amount of research focused on grading compared to course design and courseevaluation, and that research in all these areas is increasing over time. However, we note alack of research investigating the practical usage of these tools and techniques by teachers.