NLP/AI Based Techniques for Programming Exercises Generation
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abstract
This paper focuses on the enhancement of computer programming exercises generation to the benefit
of both students and teachers. By exploring Natural Language Processing (NLP) and Machine
Learning (ML) methods for automatic generation of text and source code, it is possible to semiautomatically
construct programming exercises, aiding teachers to reduce redundant work and more
easily apply active learning methodologies. This would not only allow them to still play a leading
role in the teaching-learning process, but also provide students a better and more interactive learning
experience. If embedded in a widely accessible website, an exercises generator with these Artificial
Intelligence (AI) methods might be used directly by students, in order to obtain randomised lists of
exercises for their own study, at their own time. The emergence of new and increasingly powerful
technologies, such as the ones utilised by ChatGPT, raises the discussion about their use for exercise
generation. Albeit highly capable, monetary and computational costs are still obstacles for wider
adoption, as well as the possibility of incorrect results. This paper describes the characteristics
and behaviour of several ML models applied and trained for text and code generation and their
use to generate computer programming exercises. Finally, an analysis based on correctness and
coherence of the resulting exercise statements and complementary source codes generated/produced
is presented, and the role that this type of technology can play in a programming exercise automatic
generation system is discussed.
