AI Chatbots Challenges and Opportunities

By Jeremy Rolleston|2025-07-26T08:18:43+00:00January 8th, 2025|AI News|0 Comments

The Future Of Chatbots: Use Cases & Opportunities You Need To Know

To this end, initial codes were identified by open coding and iteratively improved through comparison, group discussion among the authors, and subsequent code expansion. Further, codes were supplemented with detailed descriptions until a saturation point was reached, where all included studies could be successfully mapped to codes, suggesting no need for further refinement. As an example, codes for RQ2 (Pedagogical Roles) were adapted and refined in terms of their level of abstraction from an initial set of only two codes, 1) a code for chatbots in the learning role and 2) a code for chatbots in a service-oriented role. After coding a larger set of publications, it became clear that the code for service-oriented chatbots needed to be further distinguished. This was because it summarized e.g. automation activities with activities related to self-regulated learning and thus could not be distinguished sharply enough from the learning role.

From this, it can be seen that Learning is the most frequently used role of the examined publications (49%), followed by Assisting (20%) and Mentoring (15%). It should be noted that pedagogical roles were not identified for all the publications examined. In the mentoring role (Mentoring), chatbot actions deal with the student’s personal development. In this type of support, the student himself is the focus of the conversation and should be encouraged to plan, reflect or assess his progress on a meta-cognitive level.

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By addressing these challenges, we believe that chatbots can become effective educational tools capable of supporting learners with informative feedback. Therefore, looking at our results and the challenges presented, we conclude, “No, we are not there yet! ” – There is still much to be done in terms of research on chatbots in education. Still, development in this area seems to have just begun to gain momentum and we expect to see new insights in the coming years. While Mentoring chatbots to support Self-Regulated Learning are intended to encourage students to reflect on and plan their learning progress, Mentoring chatbots to support Life Skills address general student’s abilities such as self-confidence or managing emotions.

But research also shows some people interacting with these chatbots actually prefer the machines; they feel less stigma in asking for help, knowing there’s no human at the other end. Picard, for example, is looking at various ways technology might flag a patient’s worsening mood — using data collected from motion sensors on the body, activity on apps, chatbot challenges or posts on social media. Once you equip your chatbot to handle low-value, high-volume enquiries, start gradually introducing progressively more complex customer support tasks. With this in mind, many businesses will be fighting a strong urge to use bots as just another channel to send push notifications, repurposed content, and SPAM through.

Challenge 6: Viability of data

Human beings need to respond with an appropriate message, which should look like a natural reply. ML algorithms break down your queries or messages into human understandable natural languages with NLP techniques and send the response similar to what you expect from a human on the other side. A. Kuki or Mitsuku is the most intelligent chatbot, according to Google AI research. It has won the Loebner Prize Turing Test five times for being the best conversational chatbot in the world. – They are susceptible to data security breaches.– They can misunderstand user sentiment.– They can face vernacular issues.– They can interrupt the user experience.

Others distinguish between conversational agents for goal completion versus social chatter, referring only to the latter as chatbots (e.g. Jurafsky and Martin 61). However, in consequence of the rapid evolvement both in technology, services, and patterns of use, we find such attempts at principled scoping of the chatbot term challenging. For example, there is often no clear distinction between social chatter and goal-orientation in conversational agents—as seen by the importance of social responses for customer service chatbots [114]. Likewise, the distinction between text and voice is less than clear-cut as the same conversational agents may make use of different modalities [97]. Conversational AI solutions—including chatbots, virtual agents, and voice assistants—have become extraordinarily popular over the last few years, especially in the previous year, with accelerated adoption due to COVID-19. Data from various conversational AI vendors showed that the volume of interactions handled by conversational agents increased by as much as 250% in multiple industries.6 These solutions are already delivering significant value for many organizations.

Research related to chatbots is also conducted in multiple communities with varying degrees of exchange among them. These communities may not label their area of interest as chatbot research but, for example, research addressing conversational agents [79], dialogue systems [59] or social robotics [93]. The research objectives within these communities may only be partially overlapping. However, we believe these communities likely will benefit from strengthening their collaboration and mutually inform and support each other’s research. In consequence, our use of the term chatbot is broader than what may be found in other research streams. For example, some distinguish between voice-based and text-based conversational agents, using the term chatbot to refer to the latter, (e.g. Ashktorab et al. 6).