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HBO/WO

MBO

8 april 2026

ACA-bot

ACA-bot is an engaging chatbot designed for Automated Conversational Assessment (ACA). Drawing inspiration from traditional oral exams, this text-based conversational agent transforms assessment into a dynamic learning experience. It supports both formative and summative evaluation by offering students personalised feedback and guidance at every step of their learning journey. The first ACA-bot prototype focuses on assessing students’ understanding of basic statistical concepts, but its flexible design allows for easy adaptation to a wide range of subjects and learning contexts. Through interactive dialogue, students can practise explaining ideas clearly under exam-like conditions, apply their knowledge to new and challenging questions, and refine their reasoning as they respond to follow-up prompts. They gain valuable insights into their own understanding, identify areas for improvement, and gradually build confidence for both oral and written assessments. For educators, ACA-bot offers a powerful way to scale oral examinations and enhance formative assessment without increasing workload. It automates parts of the assessment process, provides structured and actionable insights into student performance, and opens the door to more personal, interactive feedback at scale. By combining the strengths of conversational AI and educational assessment, ACA-bot turns evaluation into a collaborative, insightful process. It empowers students to think aloud, reason more effectively, and grow, while giving educators a smarter, more innovative way to understand and support learning. ACA-bot is een slimme en inspirerende chatbot die onderwijs en Artificial Intelligence samenbrengt. Geïnspireerd door het klassieke mondelinge examen tilt deze tekstgebaseerde gespreksagent Automated Conversational Assessment naar een nieuw niveau. Studenten krijgen via natuurlijke gesprekken directe, persoonlijke feedback en begeleiding, waardoor leren en toetsen samenvloeien in één boeiende ervaring. Het prototype van ACA-bot is ontwikkeld om kennis over statistiek te beoordelen, maar dankzij het flexibele ontwerp kan het systeem eenvoudig worden aangepast aan andere vakgebieden en toepassingen. Tijdens interactieve dialogen leren studenten helder uitleg geven, hun kennis toepassen op nieuwe vragen en hun redenering aanscherpen. Ze ontdekken waar hun begrip nog verdiept kan worden en groeien zichtbaar in zelfvertrouwen — waardevolle voorbereiding op zowel mondelinge als schriftelijke examens. Ook voor docenten biedt ACA-bot een slimme oplossing. Het maakt mondelinge toetsing schaalbaar, ondersteunt formatieve beoordeling via gesprek, en vermindert de werkdruk door delen van het beoordelingsproces te automatiseren. Bovendien geeft het gestructureerde inzichten in de voortgang en denkwijze van studenten, waardoor elke dialoog bijdraagt aan beter onderwijs. ACA-bot laat zien hoe Automated Conversational Assessment het leren persoonlijker, interactiever en effectiever maakt. Studenten denken hardop, verkennen hun eigen begrip, en verbeteren met vertrouwen — terwijl docenten op een innovatieve, datagedreven manier inzicht krijgen in hoe kennis werkelijk wordt begrepen.

App

Aki Härmä, Julie Bauer

1 december 2023

ICF in Person-centred Rehabilitation

The International Classification of Functioning, Disability and Health (ICF) is a linking framework for interprofessional collaboration and structuring the needs of a client. The person's functioning is the starting point for person- and goal-oriented iterative rehabilitation, for which ICF applications are needed. This material aims to support the use of the ICF in person-centred rehabilitation and interprofessional practice by offering ICF tools. It is based on the MAGPIED rehabilitation process: Meet, Assess, Goal set, Plan, Implement, Evaluate, and Document. Even though the material follows the rehabilitation model, it can be used by any professionals who uses the ICF. The idea of the material is to offer a short theoretical background, examples, and tools as well as examples of assignments for the reader. It can be used by any professionals as an inspiring package of ICF tools or supporting material when educating the usage of ICF in higher education institutions. For the lectures, the material includes a supplement PowerPoint presentation for the Assess and Plan phases.

Document

Jaana Paltamaa, Anu Myllyharju-Puikkonen

23 augustus 2023

Lecture: Understanding data

Learner acquires demonstrable knowledge of data forms used in AI. Learner can talk about data using the right terminology (e.g., features, variables, observations). Learner knows the different data types and knows how to transform them (e.g., nominal, ordinal). Learner knows the most common measures for data description (e.g., mean, median, standard deviation) and knows how to calculate them. Learner acquires demonstrable knowledge of how data are described via measures and visualisation. Learner knows the most common graphs and knows how to pick a visualisation, matching the given data.

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HCAIM Consortium

23 augustus 2023

Lecture: Exploratory Data Analysis

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

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HCAIM Consortium

23 augustus 2023

Interactive session: Risk & Risk mitigation

Recognize, analyze and discuss the necessity and the role of security testing in AI. Employ documents, such as the AI Act, to classify use cases into risk categories. Discuss in-depth and with common European vocabulary on AI risk and how can AI be designed safely and benevolently, in ways that will not expose humans to unacceptable risks of harm, even when risky behaviour may advance a system goal. Classify the obligations of providers and users in relation to AI risk management. Discuss the requirements for high-risk AI systems as per the AI Act. Elaborate and recognize specific risks and risk taxonomies, relevant throughout the different stages of the AI lifecycle.

Website

HCAIM Consortium

2023

Lecture Notes chapter 2: Introduction to Python Programming

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 2 of the lecture notes introduces the Python programming language. It consists of four sub-chapters, each published as separate files. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize these .ipynb files. But the attached PDFs will give an idea of their content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

8 mei 2018

10-20: Data analysis (descriptive)- micro lecture VARIANCE AND STANDARD DEVIATION

In this micro lecture the following items will be explained regarding this lecture: In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved

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Henk van der Kolk

8 mei 2018

10-25: Data analysis (descriptive)- ASSIGNMENT 10: SUMMARIZING RATIO VARIABLES

In this assignment the following items will be explained regarding this assignment: In this assignment you will learn how to omprehend the meaning and implications of basic summary measures for ratio variables. Mean, median, quartiles, interquartile range,

Document

Henk van der Kolk

8 mei 2018

06-09: Scale index construction and assessment - micro lecture IDENTIFYING PROBLEMATIC SCALE ITEMS

In this micro lecture the following items will be explained regarding this lecture: Know the criteria for a good scale Know how to identify items that might be problematic Know possible reasons for problematic items Know how to improve problematic items

Website

Henk van der Kolk

8 mei 2018

10-10: Data analysis (descriptive)- micro lecture IQR AND BOX PLOT

In this micro lecture the following items will be explained regarding this lecture: In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved.

Website

Henk van der Kolk