**7. Conclusions**

The expert system control mechanisms adapt navigation links that minimize differences between information foraging values and meaning formation (determined by transition rules specified by Eq. (4)), as well as by using the neural net classifier information (successful vs.

The expert system includes a PROLOG backward-chaining inference engine that allows the system to build a valid "mental representation (GOAL)" based on natural semantic net data structures in the knowledge domain (templates) by request of a decision rule. This rule system considers whether schema priming for a specific module has been achieved by consulting the neural net classifier and by comparing the obtained path M values against an ideal descending organization of M values. If a semantic effect is not obtained, then the following

**2.** The system instructs the inference engine to use the database to construct the closest mental representation based on the user's concept path (link set). Then, the navigation is modified based on the template that best approximates the user's initial exploration, and the

Currently, research is being performed to achieve a dynamic optimization of search information by adapting navigational support based on minimization of differences between meaning values of the user and knowledge domains rather than waiting for the user to complete a

An adaptive e-instruction system (AHS/hypertext) within the present scope requires a database containing natural semantic networks similar to those described earlier. Here, templates are data structures containing SAM groups and their semantic values in which information can be accessed by a PROLOG-based inference engine. As the sample for developing these SAM groups is enlarged, better predictions for adapting navigational support

As shown in **Figure 11A**, when a student begins a learning session, she/he is presented with a

Before and after exploring each module, a semantic priming study must be performed to provide the expert system module with information for adapting the navigation support by modifying the link structure in a module based on a meaning formation template. After a

unsuccessful integration of information in the user's lexicon).

82 From Natural to Artificial Intelligence - Algorithms and Applications

**1.** The subsequent knowledge modules remain disabled.

**6.3. Expert model: inference engine**

user is prompted to try again.

menu of options of the course content.

events occur:

knowledge module.

can be achieved.

**6.5. Interface**

**6.4. Knowledge domain**

The goal of the proposed system is to promote assessment tasks as learning tasks, student involvement in assessment, and forward-looking feedback in adaptive e-instruction systems [32]. This system reduces the enormous delay in e-assessment innovation: e-assessment has been limited to mere digitization of traditional, sometimes ancient, evaluation methods [35].

A new empirical research line is opened in which student modeling is improved by using tools of cognitive science in adaptive e-learning systems in ways that were not possible before. We believe that research exploring the human lexicon as a way to adapt instruction will be at the center of future developments in AHS/hypertext.
