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Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.

Learning processes are impacted by individuals’ capabilities (e.g., working memory capacity). Nowadays, the technological improvements like those promised by deep learning methods open new opportunities to adapt learning environments. However, studies using the abilities of these models to individualize learning remain currently rare. The current research project aims to tackle one of the major constraints imposed by video learning related to the transitory aspect of delivered information. The purpose is to individualize the learning, based on learners working memory capacity (WMC). To this end, several elements need to be taken into account: (1) How to automatically and efficiently evaluate WMC without impacting learning; (2) Analyze in details the actual effect of WMC on video learning; (3) Get the most efficient and acceptable solutions to reduce the effects of WMC.

Learning processes are impacted by individuals’ capabilities (e.g., working memory capacity). Nowadays, the technological improvements like those promised by deep learning methods open new opportunities to adapt learning environments. However, studies using the abilities of these models to individualize learning remain currently rare. The current research project aims to tackle one of the major constraints imposed by video learning related to the transitory aspect of delivered information. The purpose is to individualize the learning, based on learners working memory capacity (WMC). To this end, several elements need to be taken into account: (1) How to automatically and efficiently evaluate WMC without impacting learning; (2) Analyze in details the actual effect of WMC on video learning; (3) Get the most efficient and acceptable solutions to reduce the effects of WMC.

Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.