Assisted Measurement of Input Complexity with TextInspector AI for Reading Comprehension Design in English Philology University Framework

Enfoque

Introduction:

The potential applications of Artificial Intelligence technologies, which are commonly denominated under the English acronym label of “AI”, in education are subject to a steady process of discovery. Similarly, the outreach of the growing intervention of these pieces of neuron-cloud-powered engines remains simply unpredictable.

Within the human-resource exhausting framework of university teaching, where students typically outnumber teachers, preventing, thus, the customisation of the teaching-learning process to every pupil’s particular circumstances, conditions and learning styles, AI are being deployed to alleviate bureaucracy and mechanise complex tasks. Material selection, which befits a time-consuming, yet vital, constituent of the teaching experience is no exception to this phenomenon.

 

Research goals:

The main goal of this conference paper is to explore the innovative deployment TextInspector as input selection in English Studies university degrees.

 

Discussion and results:

In these university programmes, the weight of modules oriented at the metalinguistic acquisition of communicative competence, as measured by means of the learning indicators outlined by the Common European Framework of Reference for Languages is far from disdainable. The importance of these subjects within the study plan is central, since they stand as the vertical axis for the progression of students within the discovery of the most salient linguistic, literary, cinematographic and cultural aspects of major English-speaking communities. Consequently, the materials selected or designed to aid students in the acquisition of the scope contents and skills are pivotal for the appropriate scaffolding of learning within pupils’ minds. The adequacy of complexity of the teaching resources may either contribute or hinder students’ progress, which justifies the suitability of the existence of CEFRL system.

To these regards, TextInspector analyses the convergence of syntactic and structural items, lexical variety, lexical complexity and the intratextual correlation of thematic elements into the body text to assess the suitability of reading and listening texts for comprehension within any given goal language level.

 

Concluding remarks:

As defended in this innovation proposal, TextInspector reveals as a text analysis AI which may assist teachers in material selection. The adequacy of English language input of paramount importance for the sufficient language training of students at English studies programmes but may consume excessive teachers’ times and energies for a systematic delimitation of the resources to provide pupils with.