Reinforcement or Scaffolding? A Comparative Analysis of AI-Driven Language Interaction through the Lenses of Skinner and Bruner

The current study provides a comparative analysis of two major theories of learning – the Reinforcement Theory by B.F. Skinner and the Scaffolding Framework of Jerome Bruner within the framework of the use of AI-based language interaction in Teaching English as a Foreign Language (TEFL). With increasing incorporation of AI technologies in language learning, there is a need to explore how these theories could be applied in AI technologies of language learning. Based on major empirical and theoretical works in selected reputable sources, this research analyzes the use of reinforcement and scaffolding principles in AI systems for language learning. It was found out that reinforcement mechanisms in Skinner's theory, which are based on behaviorist stimulus-response paradigm, take the form of reward-based feedback, error correction, and difficultly level adaptation in AI systems. In contrast, scaffolding principle in Bruner's theory, based on constructivist and sociocultural perspectives, takes the form of dialogue system, context-dependent prompts, and cognitive assistance in AI systems. The study emphasizes complementary nature of the use of reinforcement and scaffolding in AI-based language learning, which provides potential for integration of reinforcement and scaffolding principles in dynamic AI-based learning process to increase its motivational value.

کلیدواژه ها:

reinforcement ، scaffolding ، artificial intelligence ، language learning ، second language acquisition ، educational technology